Targets in breast cancer for prognosis or therapy

ABSTRACT

Cancer markers are developed to detect diseases characterized by increased expression of apoptosis-suppressing genes, such as aggressive cancers. Genome wide analyses of genome copy number and gene expression in breast cancer revealed 66 genes in the human chromosomal regions, 8p11, 11q13, 17q12, and 20q13 that were amplified. Diagnosis and assessment of amplification levels of genes shown to be amplified are useful in prediction of patient outcome of a of patient&#39;s response and drug resistance in breast cancer. Certain genes were found to be high priority therapeutic targets by the identification of recurrent aberrations involving genome sequence, copy number and/or gene expression are associated with reduced survival duration in certain diseases and cancers, specifically breast cancer. Inhibitors of these genes will be useful therapies for treatment of these non-responsive cancers.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation-in-part of PCT application no. PCT/US2007/070908, filed Jun. 11, 2007, which claims priority to U.S. provisional patent application No. 60/812,704, filed on Jun. 9, 2006, each of which applications is hereby incorporated by reference in its entirety.

STATEMENT OF GOVERNMENTAL SUPPORT

This invention was made during work supported by the National Cancer Institute, through Grants CA 58207 and CA 112970, and during work supported by the U.S. Department of Energy under Contract No. DE-AC03-76SF00098, now DE-AC02-05CH11231. The government has certain rights in this invention.

REFERENCE TO ATTACHED TABLES AND SEQUENCE LISTINGS

This application incorporates by reference in their entirety the attached tables and sequence listing. Tables 1-6 and 8-10 are appended after the claims and are incorporated by reference. The content of the accompanying sequence listing is incorporated herein by reference in its entirety.

FIELD OF THE INVENTION

The present invention relates to markers and chromosomal amplification correlated to disease, particularly malignant disease such as breast cancer. More specifically, the present invention relates to using cancer markers and chromosomal region analyses for the prediction of patient outcome in breast cancer patients. The present invention also relates to markers and therapeutics targeting in vivo drug resistance. More specifically, the present invention relates to the diagnosis and treatment using cancer markers and therapeutics which target drug resistance in breast cancer patients with low survival rates.

BACKGROUND OF THE INVENTION

Breast cancer is one of the most common malignancies among women and shares, together with lung carcinoma, the highest fatality rate of all cancers affecting females. The current treatment of the breast cancer is limited to a very invasive, total or partial mastectomy, radiation therapy, or chemotherapy, later two resulting in serious undesirable side effects.

It is now well established that breast cancers progress through accumulation of genomic (Albertson et al., 2003; Knuutila et al., 2000) and epigenomic (Baylin and Herman, 2000; Jones, 2005) aberrations that enable development of aspects of cancer pathophysiology such as reduced apoptosis, unchecked proliferation, increased motility, and increased angiogenesis (Hanahan and Weinberg, 2000). Discovery of the genes that contribute to these pathophysiologies when deregulated by recurrent aberrations is important to understanding mechanisms of cancer formation and progression and to guide improvements in cancer diagnosis and treatment.

Analyses of expression profiles have been particularly powerful in identifying distinctive breast cancer subsets that differ in biological characteristics and clinical outcome (Perou et al., 1999; Perou et al., 2000; Sorlie et al., 2001; Sorlie et al., 2003). For example, unsupervised hierarchical clustering of microarray derived expression data have identified intrinsically variable gene sets that distinguish five breast cancer subtypes—basal-like, luminal A, luminal B, ERBB2 and normal breast-like. The basal-like and ERBB2 subtypes have been associated with strongly reduced survival durations in patients treated with surgery plus radiation (Perou et al., 2000; Sorlie et al., 2001) and some studies have suggested that reduced survival duration in poorly performing subtypes is caused by an inherently high propensity to metastasize (Ramaswamy et al., 2003). These analyses already have led to the development of multi-gene assays that stratify patients into groups that can be offered treatment strategies based on risk of progression (Esteva et al., 2005; Gianni et al., 2005; van 't Veer et al., 2002). However, the predictive power of these assays is still not as high as desired and the assays have not been fully tested in patient populations treated with aggressive adjuvant chemotherapies.

Analyses of breast tumors using fluorescence in situ hybridization (Al-Kuraya et al., 2004; Kallioniemi et al., 1992; Press et al., 2005; Tanner et al., 1994) and comparative genomic hybridization (Kallioniemi et al., 1994; Loo et al., 2004; Naylor et al., 2005; Pollack et al., 1999) show that breast tumors also display a number of recurrent genome copy number aberrations including regions of high level amplification that have been associated with adverse outcome (Al-Kuraya et al., 2004; Cheng et al., 2004; Isola et al., 1995; Jain et al., 2001; Press et al., 2005). This raises the possibility of improved patient stratification through combined analysis of gene expression and genome copy number (Barlund et al., 2000; Pollack et al., 2002; Ray et al., 2004; Yi et al., 2005). In addition, several studies of specific chromosomal regions of recurrent abnormality at 17q12 (Kauraniemi et al., 2001; Kauraniemi et al., 2003) and 8p11 (Gelsi-Boyer et al., 2005; Ray et al., 2004) show the value of combined analysis of genome copy number and gene expression for identification of genes that contribute to breast cancer pathophysiology by deregulating gene expression.

Nevertheless, there is a continued need for further understanding of the genes, and of the chromosomal aberration(s) that occur in cancer, for example breast cancer.

BRIEF SUMMARY OF THE INVENTION

Disclosed herein are roles of genome copy number abnormalities (CNAs) in breast cancer pathophysiology by identifying associations between recurrent CNAs, gene expression and clinical outcome in a set of aggressively treated early stage breast tumors. It shows that the recurrent CNAs differ between tumor subtypes defined by expression pattern and that stratification of patients according to outcome can be improved by measuring both expression and copy number; especially high level amplification. Sixty-six genes (set forth in Table 3) deregulated by the high level amplifications are therapeutic targets; nine of these genes (FGFR1, IKBKB, ERBB2, PROSC, ADAM9, FNTA, ACA CA, PNMT, and NR1D1) are “druggable.” Low level CNAs appear to contribute to cancer progression by altering RNA and cellular metabolism.

As used herein gene amplification is used in a broad sense. It comprises an increase of gene copy number; it can also comprise assessment amplification of the gene product. Thus levels of gene expression, as well as corresponding protein expression can be evaluated. In the embodiments that follow, it is understood that assessment of gene expression can be used to assess level of gene product such as RNA or protein.

Thus, embodiments of the invention include: A method for prognosing the outcome of a patient with breast cancer, said method comprising: providing breast cancer tissue from the patient; determining from the provided tissue, the level of gene amplification or gene expression for at least one gene set forth in Table 3; identifying that the at least one gene or gene product is amplified; whereby, when the at least one gene or gene product is amplified, this is an indication that the patient has the predicted disease free survival or probability for distant recurrence set forth in Table3. This method can comprise that the gene or gene product is ACACA (SEQ ID NOs: 1, 2), ADAM9 (SEQ ID NOs: 3-8), ERBB2 (SEQ ID NOs: 9-14), FGFR1 (SEQ ID NOs: 15, 16), FNTA (SEQ ID NOs: 17, 18), IKBKB (SEQ ID NOs: 19, 20), NR1D1 (SEQ ID NOs: 21, 22), PNMT (SEQ ID NOs: 23, 24), or PROSC (SEQ ID NOs: 25, 26); in particular PROSC (SEQ ID NOs: 25, 26), ADAM9 (SEQ ID NOs: 3-8), FNTA (SEQ ID NOs: 17, 18), ACACA (SEQ ID NOs: 1, 2), PNMT (SEQ ID NOs: 23, 24), or NR1D1 (SEQ ID NOs: 21, 22). In one preferred embodiment, the gene, ADAM9 (SEQ ID NOs: 3, 5 and 7) is a therapeutic target. In certain embodiments, there is a proviso that the gene or gene product is not ERBB2 (SEQ ID NOs: 9-14), FGFR1 (SEQ ID NOs: 15, 16), or IKBKB (SEQ ID NOs: 19, 20). The detecting step can comprise use a of methodology selected from the group consisting of quantitative PCR, FISH, array CGH, quantitative PCR, in situ hybridization for RNA, immunohistochemistry and reverse phase protein lysate arrays for protein. In some embodiments, the gene or gene product is FGF3 (SEQ ID NOs: 65, 66), PPFIA1 (SEQ ID NOs: 69, 70), NEU3 (SEQ ID NOs: 79, 80), CSTF1 (SEQ ID NOs: 117, 118), PCK1 (SEQ ID NOs: 123, 124), VAPB (SEQ ID NOs: 129, 130), GNAS (SEQ ID NOs: 135, 136), BCAS1 (SEQ ID NOs: 115, 116), TMEPA1 (SEQ ID NOs: 125, 126), or STX16 (SEQ ID NOs: 131, 132). In certain embodiments, the gene or gene product is FGF3 (SEQ ID NOs: 65, 66), PPFIA1 (SEQ ID NOs: 69, 70), NEU3 (SEQ ID NOs: 79, 80), GNAS (SEQ ID NOs: 135, 136), TMEPA1 (SEQ ID NOs: 125, 126), STX16 (SEQ ID NOs: 131, 132), or VAPB (SEQ ID NOs: 129, 130). In some embodiments, the breast cancer is a luminal A breast cancer and the gene or gene product is a gene or encoded by a gene at 11q13-14 and/or 20q13, e.g., FGF3 (SEQ ID NOs: 65, 66), PPFIA1 (SEQ ID NOs: 69, 70), NEU3 (SEQ ID NOs: 79, 80), GNAS (SEQ ID NOs: 135, 136), TMEPA1 (SEQ ID NOs: 125, 126), STX16 (SEQ ID NOs: 131, 132), or VAPB (SEQ ID NOs: 129, 130).

An embodiment in accordance with the invention comprises: A method for selecting a patient for treatment with a drug that modulates the expression of a gene set forth in Table 3, said method comprising: providing tissue biopsy from the patient; determining from the provided tissue, the level of gene amplification or gene product expression for a gene set forth in Table 3; identifying that one or more of the genes or gene products is amplified; whereby, when the one or more genes or gene products are amplified, this gene and/or gene product is a candidate for treatment with a drug that modulates the expression of the one or more gene of Table 3 or a drug that affects a protein of Table 3. In certain embodiments, the gene or product is ACACA (SEQ ID NOs: 1, 2), ADAM9 (SEQ ID NOs: 3-8), ERBB2 (SEQ ID NOs: 9-14), FGFR1 (SEQ ID NOs: 15, 16), FNTA (SEQ ID NOs: 17, 18), IKBKB (SEQ ID NOs: 19, 20), NR1D1 (SEQ ID NOs: 21, 22), PNMT (SEQ ID NOs: 23, 24), or PROSC (SEQ ID NOs: 25, 26); in particular PROSC (SEQ ID NOs: 25, 26), ADAM9 (SEQ ID NOs: 3-8), FNTA (SEQ ID NOs: 17, 18), ACACA (SEQ ID NOs: 1, 2), PNMT (SEQ ID NOs: 23, 24), or NR1D1 (SEQ ID NOs: 21, 22).; and in one embodiment, particularly, ADAM9. In certain embodiments, there is a proviso that the gene or gene product is not ERBB2 (SEQ ID NOs: 9-14), FGFR1 (SEQ ID NOs: 15, 16), or IKBKB (SEQ ID NOs: 19, 20). In some embodiments, the gene or gene product is FGF3 (SEQ ID NOs: 65, 66), PPFIA1 (SEQ ID NOs: 69, 70), NEU3 (SEQ ID NOs: 79, 80), CSTF1 (SEQ ID NOs: 117, 118), PCK1 (SEQ ID NOs: 123, 124), VAPB (SEQ ID NOs: 129, 130), GNAS (SEQ ID NOs: 135, 136), BCAS1 (SEQ ID NOs: 115, 116), TMEPA1 (SEQ ID NOs: 125, 126), or STX16 (SEQ ID NOs: 131, 132). In certain embodiments, the gene or gene product is FGF3 (SEQ ID NOs: 65, 66), PPFIA1 (SEQ ID NOs: 69, 70), NEU3 (SEQ ID NOs: 79, 80), GNAS (SEQ ID NOs: 135, 136), TMEPA1 (SEQ ID NOs: 125, 126), STX16 (SEQ ID NOs: 131, 132), or VAPB (SEQ ID NOs: 129, 130). In some embodiments, the breast cancer is a luminal A breast cancer and the gene or gene product is a gene or encoded by a gene at 11q13-14 and/or 20q13, e.g., FGF3 (SEQ ID NOs: 65, 66), PPFIA1 (SEQ ID NOs: 69, 70), NEU3 (SEQ ID NOs: 79, 80), GNAS (SEQ ID NOs: 135, 136), TMEPA1 (SEQ ID NOs: 125, 126), STX16 (SEQ ID NOs: 131, 132), or VAPB (SEQ ID NOs: 129, 130). The determining step can comprise use a of methodology selected from the group consisting of quantitative PCR, FISH, array CGH, quantitative PCR, in situ hybridization for RNA, immunohistochemistry and reverse phase protein lysate arrays for protein.

An embodiment of the invention comprises: A method for treatment of a patient with breast cancer, said method comprising: providing tissue biopsy from the patient; determining from the provided tissue, the level of gene amplification or level of gene product for a gene set forth in Table 3; identifying that one or more of the genes or gene products is amplified; whereby, when the one or more genes or gene products are amplified, this patent is treated with a drug that modulates the expression of the one or more gene or a drug that affects the gene product. In certain embodiments, the gene or gene product is ACACA (SEQ ID NOs: 1, 2), ADAM9 (SEQ ID NOs: 3-8), ERBB2 (SEQ ID NOs: 9-14), FGFR1 (SEQ ID NOs: 15, 16), FNTA (SEQ ID NOs: 17, 18), IKBKB (SEQ ID NOs: 19, 20), NR1D1 (SEQ ID NOs: 21, 22), PNMT (SEQ ID NOs: 23, 24), or PROSC (SEQ ID NOs: 25, 26); in particular PROSC (SEQ ID NOs: 25, 26), ADAM9 (SEQ ID NOs: 3-8), FNTA (SEQ ID NOs: 17, 18), ACACA (SEQ ID NOs: 1, 2), PNMT (SEQ ID NOs: 23, 24), or NR1D1 (SEQ ID NOs: 21, 22).; or more particularly in one embodiment, ADAM9. In certain embodiments there is a proviso that the gene or gene product is not ERBB2(SEQ ID NOs: 9-14), FGFR1 (SEQ ID NOs: 15, 16), or IKBKB (SEQ ID NOs: 19, 20). In some embodiments, the gene or gene product is FGF3 (SEQ ID NOs: 65, 66), PPFIA1 (SEQ ID NOs: 69, 70), NEU3 (SEQ ID NOs: 79, 80), CSTF1 (SEQ ID NOs: 117, 118), PCK1 (SEQ ID NOs: 123, 124), VAPB (SEQ ID NOs: 129, 130), GNAS (SEQ ID NOs: 135, 136), BCAS1 (SEQ ID NOs: 115, 116), TMEPA1 (SEQ ID NOs: 125, 126), or STX16 (SEQ ID NOs: 131, 132). In certain embodiments, the gene or gene product is FGF3 (SEQ ID NOs: 65, 66), PPFIA1 (SEQ ID NOs: 69, 70), NEU3 (SEQ ID NOs: 79, 80), GNAS (SEQ ID NOs: 135, 136), TMEPA1 (SEQ ID NOs: 125, 126), STX16 (SEQ ID NOs: 131, 132), or VAPB (SEQ ID NOs: 129, 130). In some embodiments, the breast cancer is a luminal A breast cancer and the gene or gene product is a gene or encoded by a gene at 11q13-14 and/or 20q13, e.g., FGF3 (SEQ ID NOs: 65, 66), PPFIA1 (SEQ ID NOs: 69, 70), NEU3 (SEQ ID NOs: 79, 80), GNAS (SEQ ID NOs: 135, 136), TMEPA1 (SEQ ID NOs: 125, 126), STX16 (SEQ ID NOs: 131, 132), or VAPB (SEQ ID NOs: 129, 130). In one embodiment the drug is an antisense sequence for a gene of Table 3, and the particular antisense sequence corresponds to the one or more amplified genes identified in the identifying step. The determining step can comprise use a of methodology selected from the group consisting of quantitative PCR, FISH, array CGH, quantitative PCR, in situ hybridization for RNA, immunohistochemistry and reverse phase protein lysate arrays for protein.

Another embodiment of the invention comprises: A method for identifying a moiety that modulates a protein, said method comprising: providing a protein selected from the group consisting of PROSC (SEQ ID NO: 26), ADAM9 (SEQ ID NOs: 4, 6, or 8), FNTA (SEQ ID NO: 18), ACACA (SEQ ID NO: 2), PNMT (SEQ ID NO: 24), or NR1D1 (SEQ ID NO: 22); screening the provided protein with a candidate moiety; determining whether the candidate moiety modules (e.g., alters function or expression) of the protein; and, selecting a moiety that modules the protein. A further embodiment comprises: A method for modulating a PROSC (SEQ ID NO: 26), ADAM9 (SEQ ID NOs: 4, 6 or 8), FNTA (SEQ ID NO: 18), ACACA (SEQ ID NO: 2), PNMT (SEQ ID NO: 24), or NR1D1 (SEQ ID NO: 22) protein in a living cell, said method comprising: providing a moiety that modulates the protein; administering the moiety to a living cell that expresses PROSC, ADAM9, FNTA, ACACA, PNMT, or NR1D1 protein corresponding to the moiety; whereby, PROSC, ADAM9, FNTA, ACACA, PNMT, or NR1D1 protein in the cell is modulated.

Another embodiment of the invention comprises a method for prognosing the outcome of a patient with breast cancer, said method comprising: providing breast cancer tissue from the patient; determining from the provided tissue, the level of gene deletion for at least one gene from amplicon 8p11-12; identifying that the at least one gene is deleted; whereby, when the at least one gene is deleted, this is an indication that the patient has the predicted disease free survival or probability for distant recurrence set forth in Table3. In certain embodiments, the at least one gene from amplicon 8p11-12 is selected from the chromosome 8 genes set forth in Table 3. The determining step can comprise use a of methodology selected from the group consisting of quantitative PCR, FISH, array CGH, quantitative PCR, in situ hybridization for RNA, immunohistochemistry and reverse phase protein lysate arrays for protein.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1. Recurrent abnormalities in 145 primary breast tumors.

FIG. 1( a). Frequencies of genome copy number gain and loss plotted as a function of genome location with chromosomes 1pter to the left and chromosomes 22qter and X to the right. Vertical lines indicate chromosome boundaries and vertical dashed lines indicate centromere locations. Positive and negative values indicate frequencies of tumors showing copy number increases and decrease respectively with gain and loss as described in the methods.

FIG. 1( b). Frequencies of tumors showing high level amplification. Data are displayed as described in FIG. 1( a).

FIG. 1( c-j). Frequencies of tumors showing significant copy number gains and losses as defined in FIG. 1( a) (upper member of each pair) or high level amplifications as defined in FIG. 1( b) (lower member of each pair) in tumor subtypes defined according to expression phenotype; FIG. 1( c) & FIG. 1( d), basal-like; FIG. 1( e) & FIG. 1( f), ERBB2;

FIG. 1( g) & FIG. 1( h), luminal A; FIG. 1( i) & FIG. 1( j), luminal B. Data are displayed as described in FIG. 1( a).

FIG. 2. Unsupervised hierarchical clustering of genome copy number profiles measured for 145 primary breast tumors. Green indicates increased genome copy number and red indicates decreased genome copy number. The three major genomic clusters from left to right are designated 1q/16q, Complex and Amplifying. The bar to the left indicates chromosome locations with chromosome 1pter to the top and 22qter and X to the bottom. The locations of the odd numbered chromosomes are indicated. The upper color bars indicate biological and clinical aspects of the tumors. Color codes are indicated at the bottom of the figure. Dark blue indicates positive status, light blue indicates negative status for Nodes, ER, PR and p53 expression. For Ki67, dark blue=fraction >0.1 and light blue=fraction <0.1. For size, light blue indicates size <2.2 cm, and dark blue indicates size >2.2 cm. Color codes for the expression bar are orange=luminal A, dark blue=normal breast-like, light blue=ERBB2, green=basal-like, yellow=luminal B.

FIG. 3. Kaplan Meyer plots showing survival in breast tumor subclasses.

FIG. 3( a). Disease specific survival in 130 breast cancer patients whose tumors were defined using expression profiling to be basal-like (third curve down), luminal A (top curve), luminal B (second curve from top) and ERBB2 class (bottom curve).

FIG. 3( b). Disease-specific survival of patients with tumors classified by genome copy number aberration analysis as 1q/16q (top curve), Complex (red) and Amplifying (blue).

FIG. 3( c). Survival of patients with (bottom curve) and without (top curve) amplification at any region of recurrent amplification.

FIG. 3( d). Survival of patients whose tumors were defined using expression profiling to be luminal A tumors with (bottom curve) and without (top curve) amplification at 8p11-12, 11q13, and/or 20q.

FIG. 3( e). Survival of patients whose tumors that were not amplified at 8p11-12 and that had normal (top curve) or reduced (bottom curve) genome copy number at 8p11-12.

FIG. 3( f). Survival of patients whose tumors had normal (top curve) or abnormal (bottom curve) genome copy number at 8p11-12.

FIG. 4. Results of unsupervised hierarchical clustering of 130 breast tumors using intrinsically variable gene expression but excluding any transcripts whose levels were significantly associated with genome copy number. Red indicates increased expression and green indicates reduced expression.

FIG. 5. Comparison of recurrent genome aberrations in 145 primary breast tumors with low-level genome copy number aberrations selected in human mammary epithelial cells during passage through telomere crisis (Chin et al., 2004).

FIG. 5( a). Frequencies of genome copy number gain and loss plotted as described in FIG. 1( a).

FIG. 5( b). Array CGH analyses of genome copy number for human mammary epithelial cells at passages 16 and 21 before transition through telomere crisis (upper two traces) and at passages 28 and 44 after immortalization (lower two traces) (Chin et al., 2004).

FIG. 6. Unsupervised hierarchical clustering of expression profiles measured for 148 tumors based on a published set of intrinsically variably genes, URL:<http://www.pnas.org/cgi/content/full 100/14/8418>, matched by UniGene ID 280 unique out of 464 gene probes in Affymetrix GeneChip). Tumor IDs under dendogram were color-coded red; basaloid, pink; ERBB2, blue; luminal A and light blue; luminal B) based on the closest distance to each subtype in 79 tumors of Stanford samples. Expression values in the cluster diagram were median centered for each gene. Similarly color-coded maker gene names for each subtype were displayed with UniGene IDs on the right of cluster diagram. These marker genes were highly expressed in the each subtype indicated in red in the cluster diagram. For redundant genes with correlation >0.45, expression values were averaged. ER positive and negative status is indicated in yellow and blue respectively under tumor ID, which corresponds well to erbB2 and basal type tumors.

FIG. 7. Graphs showing that transient transfection of siRNA for ADAM9 into (a) T47D, (b) BT549, (c) SUM52PE breast cancer cell lines strongly inhibits growth in breast cancer cells.

FIG. 8. Graphs showing that silencing of ADAM9 decreased proliferation of breast cancer cells (a) BT549 and (b) SUM52PE, but not (c) normal cells MCF10A.

FIG. 9. Down-regulation of ADAM9 by siRNA increased apoptosis in breast cancer cells as determined by detecting Yo-Pro staining.

FIG. 10. By detecting cell survival rates, growth inhibition was achieved by >30 nM siRNA in BT549 and SUM52PE breast cancer cells.

FIG. 11. siFGF3, siPPFIA1 and siNEU3 specifically inhibited cell growth in highly amplified cell lines. Cell viability was measured by the Luminescence cell viability assay (Promega Inc.) following treatment with siRNAs for 72 hours. The inhibition rate was achieved by comparison to non-target negative controls (siControl).

FIG. 12. siFGF3, siPPFIA1 and siNEU3 induced cell apoptosis in 11q13 highly amplified cell lines, but not in not-amplified cell line. Cell apoptosis was assayed using YoPro-1 and Hoechst staining with the Cellomics high content scanning instrument in 72 hours post transfection. The fold of apoptosis was achieved by normalizing to control siRNA(siControl).

FIG. 13. shRNAs can efficiently knock down FGF3, NEU3 and PPFIA1 in breast cancer cells. Protein levels were confirmed by western blot after infection of breast cancer cells with shRNAs (five shRNAs targeting different sequences/gene). Actin was the loading control. Each gene had at least one shRNA that could efficiently knock down the target gene.

FIG. 14. Knockdown of FGF3, PPFIA1 and NEU3 by shRNAs induces cell apoptosis CAMA1 cells with Caspase3 Glo assay(Promega) and YoPro/Hochest double staining.

FIG. 15. Silencing of FGF3, PPFIA1 and NEU3 by shRNAs inhibit cell growth in 3D culture. Cells that had knocked down FGF3, PPFIA1 and NEU3 proteins (with shRNAs_(#38160 shFGF3, #2969 shPPFIA1 and #5149 shNEU3 respectively) were evaluated in 3D culture. The HCC1954 and CAMA1 cells with shFGF3, shPPFIA1 and/or shNEU3 were very unhealthy and died off. The colonies were much smaller for cells with shFGF3 shPPFIA1 and/or shNEU3 than control cells. The morphology also changed compared to control cells, which had typical mass-like morphology of HCC1954 (grape like morphology of CAMA 1 cells).

FIG. 16. Synergistic effects on combinational knockdown of NEU3 and PPFIA1 genes at 11q13 amplicon. Cell viability/proliferation was evaluated by Cell Titer-Glo luminescent cell viability assay (Promega) after cells had been infected with shRNA lentivirus for 6 days. The cell viability percentage was normalized to control shRNA. Cell apoptosis was analyzed with YoPro-1 and Hoechst staining using the Cellomics high content scanning instrument after cells had been infected with shRNA lentivirus for 6 days.

FIG. 17. Candidate Therapeutics on 20q13 amplicon. The cell viability was measured by Luminescence cell viability assay (Promega Inc.) following treatment with siRNAs for 72 hours. The inhibition rate was determined in comparison to non-target negative control(siControl).

FIG. 18. GNAS, STX16, TMEPA1 and VAPB siRNAs inhibit cell proliferation by BrdU and Hoeschst staining.

FIG. 19. GNAS, STX16, TMEPA1 and VAPB siRNAs inhibit apoptosis by YoPro-1 and Hoeschst staining.

FIG. 20. Caspase3 activity increased in SUM52PE cells treated with GNAS, STX16, TMEPA1, and VAPB siRNAs.

FIG. 21. GNAS siRNAs knocked down Gs transcripts in breast cancer cell lines.

BRIEF DESCRIPTION OF THE TABLES

Table 1. Univariate and multivariate associations for individual amplicons and/or disease specific survival and distant recurrence. Also shown are the chromosomal positions of the beginning and ends of the amplicons and the flanking clones. Associations are shown for the entire sample set and for luminal A tumors (univariate associations only).

Table 2. Associations of genomic variables with clinical features.

Table 3. Functional characteristics of 66 genes; these genes are in recurrent amplicons associated with reduced survival duration in breast cancer. Functional annotation was based on the Human Protein Reference Database (http://hprd.org). Genes highlighted in dark gray are associated with reduced survival duration or distant recurrence when over expressed in non-Amplifying tumors. Genes highlighted in light gray are significantly associated with reduced survival duration or distant recurrence (p<0.05) when down regulated in non-Amplifying tumors. Distances to sites of recurrent viral integration were determined from published information (Akagi et al., 2004). The last column identifies genes having predicted protein folding characteristics indicating that they are druggable (see, e.g., Russ and Lampel, 2005).

Table 4. Univariate p-values with the corresponding 95% confidence intervals for associations with disease-specific survival and distant recurrence endpoints and the corresponding multivariate results for those found to be significant in univariate analyses (p<0.05) for at least one of the clinical end points. Only variables individually significant at p<0.05 for at least one of the two end points are included in the multivariate regression. Stage and SBR Grade are treated as continuous variables rather than factors. In each column pair, the left subcolumn lists results for disease-specific survival and the right subcolumn lists results for time to distant recurrence.

Table 5. Comparison of the association between expression subtypes and survival duration in 3 datasets. Log-likelihood ratio test p-value is shown for each model. Basal is the reference in all models. Multivariate models include size and nodal status. In multivariate analyses, the first value shown in each cell is the p-value and the second is the ratio of the medians in the compared groups.

Table 6. Identities of 1432 gene transcripts showing significant associations between genome copy numbers measured using array CGH and transcript levels measured using Affymetrix U133A expression arrays in 101 primary breast tumors. Data will be available through CaBIG and a public web site.

Table 7. The set of genes in Table 3, shown with the corresponding GenBank Accession numbers and the SEQ ID NOs assigned for the gene and gene products.

Table 8. Sequences of siRNAs targeting various human genes encoded by amplicon 11q13.

Table 9. Sequences of shRNAs targeting human NEU3, FGF3 and PPFIA1 genes.

Table 10. Sequences of siRNAs targeting various human genes encoded by amplicon 20q13.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

In order to further the understanding of the genes, and of the chromosomal aberration(s) that occur in cancer, for example breast cancer we performed combined analyses of genome copy number and gene expression to identify genes that contribute to breast cancer pathophysiology with emphasis on those that are associated with poor response to current therapies.

By associating clinical endpoints with genome copy number and gene expression, we showed strong associations between expression subtype and genome aberration composition and we identified four human chromosomal regions (8p11-12, 11q13-14, 17q12 and/or 20q13) of recurrent amplification associated with poor outcome in treated patients. Gene expression profiling revealed 66 genes (see, e.g., Table 3 and Table 7) in these regions of amplification whose expression levels were deregulated by the high-level amplifications. We also found a surprising association between low level CNAs (genome copy number abnormality CNA) and up-regulation of genes associated with RNA and protein metabolism that may suggest a new mechanism by which these aberrations contribute to cancer progression.

We disclose a comprehensive analysis of gene expression and genome copy number in aggressively treated primary human breast cancers performed in order to identify (a) genomic events that are assayed to better stratify patients according to clinical behavior, (b) identify how molecular aberrations contribute to breast cancer pathogenesis and (c) discover genes that are therapeutic targets in patients that do not respond well to current therapies.

Molecular Markers that Predict Outcome

We focused in this study on combined analyses of genome copy number and gene expression in tumors from patients who had aggressive treatment with surgery, radiation of the surgical margins, and hormonal therapy for ER positive disease and aggressive adjuvant chemotherapy as indicated (typically adriamycin and cytoxan but not including Trastuzumab). Analyses of markers in the context of this treatment regimen allowed us to identify those that predicted outcome in patients whose tumors were treated more aggressively than in previously published studies (Esteva et al., 2005; Gianni et al., 2005; van 't Veer et al., 2002). Our analyses of this aggressively treated patient cohort revealed two important associations.

First, we found that the survival of patients with tumors classified as basal-like according to expression pattern did not have significantly worse outcome than patients with luminal or normal-like tumors in this tumor set, unlike previous reports (van 't Veer et al., 2002; van de Vijver et al., 2002) (see FIG. 3( a)). However, patients with ERBB2 positive tumors did do worse (significantly increased death from disease and shorter recurrence-free survival; p<0.001 and p<0.01 respectively, log-rank test) in accordance with the earlier studies. This suggests that the aggressive chemotherapy employed for treatment of the predominantly ER negative basal-like tumors increased survival duration in these patients relative to patients with tumors in the other subgroups. Thus, outcome for patients with basal-like tumors may not be as bad as indicated by earlier prognostic studies of patient populations that did not receive aggressive chemotherapy for progressive disease. This result emphasizes the need to interpret the performance of molecular markers for patient stratification in the context of specific treatment regimens.

Secondly, we found that aggressively treated patients with high level amplification had worse outcome than did patients without amplification (see FIG. 3( c)). This is consistent with earlier CGH and single locus analyses of associations of amplification with poor prognosis (Al-Kuraya et al., 2004; Blegen et al., 2003; Callagy et al., 2005; Gelsi-Boyer et al., 2005; Weber-Mangal et al., 2003). Moreover, the presence of high level amplification was an indicator of poor outcome, even within patient subsets defined by expression profiling. This was particularly apparent for luminal A tumors as illustrated in FIG. 3( d) where patients whose tumors had high level amplification at 8p11-12, 11q13-14 or 20q13 did significantly worse than patients without amplification. This shows that that stratification according to both expression level and copy number will identify patients that respond poorly to current therapeutic treatment strategies.

Mechanisms of Disease Progression

Our combined analyses of genome copy number and gene expression showed substantial differences in recurrent genome abnormality composition between tumors classified according to expression pattern and revealed that over 10% of the genes interrogated in this study had expression levels that were highly significantly associated with genome copy number changes. Most of the gene expression changes were associated with low level changes in genome copy number, but 66 were deregulated by the high level amplifications associated with poor outcome (see Table 3), as defined as having a multiple testing corrected p-value of less than 0.05. These analyses provide evidence of: the etiology of breast cancer subtypes, mechanisms by which the low-level copy number changes contribute to cancer pathogenesis and identify a suite of genes that contribute to cancer pathophysiology when over expressed as a result of high level amplification.

Breast cancer subtypes. FIGS. 1 and 2 show that recurrent genome copy number aberrations differ substantially between tumors classified according to expression pattern as described previously (Perou et al., 1999). Basal-like tumors carried genome aberrations similar to those reported for tumors arising in BRCA1 carriers. High level amplifications were rare in these tumors. ERBB2 tumors were amplified at and distal to the ERBB2 locus on chromosome 17 but amplifications of regions on other chromosomes were rare in this tumor subset(Isola et al., 1999). Luminal A tumors carried frequent gains at 1q and 16p and losses at 16q and carried recurrent amplifications involving 8p11-12, 11q13-14, 17q11-12 and 20q13. Luminal B tumors showed many regions of genome copy number abnormality (CNA) as well as frequent amplification of three regions of chromosomes 8.

The differences in recurrent aberration composition between expression subtypes is consistent with a model of cancer progression in which the expression subtype and genotype are determined by the cell type and stage of differentiation that survives telomere crisis and acquires sufficient proliferative advantage to achieve clonal dominance in the tumor (Chin et al., 2004). This model indicates that the genome CNA spectrum is selected to be most advantageous to the progression of the specific cell type that achieves immortality and clonal dominance. In this model, the recurrent genome CNA composition can be considered an independent subtype descriptor—much as genome CNA composition can be considered to be a cancer type descriptor (Knuutila et al., 2000). The independence of the genome CNA composition and basal and luminal expression subtypes is clear from FIG. 4 which shows that the breast tumors divide into basal and luminal subtypes using unsupervised hierarchical clustering even after all transcripts showing associations with copy number are removed from the data set. Of course the ERBB2 subtype is lost since that subtype is strongly driven by ERBB2 amplification.

Low level abnormalities. The most frequent low-level copy number changes were not associated with reduced survival duration although some were associated with other markers usually associated with survival such as tumor size, nodal status, and grade (see Table 2). This raises the question of why the recurrent low-level CNAs are selected. To understand this, we applied the statistical tool GOstat to determine the ontology of the genes deregulated by these abnormalities. This analysis showed that numerous genes involved in RNA and cellular metabolism were significantly up-regulated by these events. Interestingly, we also observed that many of the recurrent low-level aberrations matched the low-level copy number changes in the ZNF217-transfected human mammary epithelial cells that emerged after passage through telomere crisis having achieved clonal dominance in the culture (see FIG. 5)—presumably because the aberrations they carried conferred a proliferative advantage (Chin et al., 2004). This indicates that the low-level CNAs contribute to early cancer formation by increasing basal metabolism thereby providing a net survival/proliferative advantage to the cells that carry them. This idea is supported by a report that some of these same classes of genes were associated with proliferative fitness yeast (Deutschbauer et al., 2005). That study described analyses of proliferative fitness in the complete set of Saccharomyces cerevisiae heterozygous deletion strains and reported reduced growth rates for strains carrying deletions in genes involved in RNA metabolism and ribosome biogenesis and assembly.

High level amplification. We found that high level amplifications of 8p11-12, 11q13-14, 17q12 and/or 20q13 were associated with reduced survival duration and/or distant recurrence overall, and within the luminal A expression subgroup. We identified 66 genes (see, e.g., Table 3) in these regions whose expression levels were correlated with copy number. These 66 genes are shown in Table 7 below along with the GenBank Accession numbers for each of the genes and gene products (proteins), the records of which are hereby incorporated by reference for all purposes. Also shown are the corresponding SEQ ID NOs as assigned here and shown in the sequence listing attached herein in computer readable form.

TABLE 7 Table 3 genes and their GenBank Accession and SEQ ID Numbers. DNA Protein GenBank PROTEIN Gene Genbank Accession No. SEQ ID NO: Accession Number SEQ ID NO: ACACA NM_198839.1 SEQ ID NO: 1 NP_942136.1 SEQ ID NO: 2 GI: 38679976 ADAM9 AF495383 SEQ ID NO: 3 AAM49575.1 SEQ ID NO: 4 var1: NM_003816; SEQ ID NO: 5 NP_003807.1 SEQ ID NO: 6 var2: NM_001005845; SEQ ID NO: 7 NP_001005845.1 SEQ ID NO: 8 ERBB2 AY208911; SEQ ID NO: 9 AAO18082.1; SEQ ID NO: 10 NM_004448; SEQ ID NO: 11 NP_004439.2; SEQ ID NO: 12 NM_001005862 SEQ ID NO: 13 NP_001005862.1 SEQ ID NO: 14 FGFR1 AY585209 SEQ ID NO: 15 NP_075599.1 SEQ ID NO: 16 FNTA NM_002027 SEQ ID NO: 17 NP_002018.1 SEQ ID NO: 18 IKBKB AY663108; or SEQ ID NO: 19 AAT65965.1, or SEQ ID NO: 20 NM_001556 XM_032491; NP_001547.1 NR1D1 NM_021724 SEQ ID NO: 21 NP_068370.1 SEQ ID NO: 22 PNMT NM_002686.3 SEQ ID NO: 23 NP_002677 SEQ ID NO: 24 PROSC NM_007198 SEQ ID NO: 25 NP_009129.1 SEQ ID NO: 26 SPFH2 AM393068 SEQ ID NO: 27 CAL37946.1 SEQ ID NO: 28 BRF2 NM_018310 SEQ ID NO: 29 NP_060780.2 SEQ ID NO: 30 RAB11FIP1 NM_001002814; SEQ ID NO: 31 NP_001002814.1 SEQ ID NO: 32 ASH2L NM_004674; SEQ ID NO: 33 NP_004665.1 SEQ ID NO: 34 LSM1 NM_014462; SEQ ID NO: 35 NP_055277.1 SEQ ID NO: 36 BAG4 NM_004874 SEQ ID NO: 37 NP_004865.1 SEQ ID NO: 38 DDHD2 NM_015214 XM_291291 SEQ ID NO: 39 NP_056029.1 SEQ ID NO: 40 WHSC1L1 NM_023034 SEQ ID NO: 41 NP_075447.1 SEQ ID NO: 42 TACC1 NM_206862 SEQ ID NO: 43 NP_996744.1 SEQ ID NO: 44 GOLGA7 NM_016099 SEQ ID NO: 45 NP_057183.2 SEQ ID NO: 46 SLD5 BC005995 SEQ ID NO: 47 AAH05995.1 SEQ ID NO: 48 MYST3 NM_006766 SEQ ID NO: 49 NP_006757.1 SEQ ID NO: 50 AP3M2 NM_006803 SEQ ID NO: 51 NP_006794.1 SEQ ID NO: 52 POLB NM_002690 SEQ ID NO: 53 NP_002681.1 SEQ ID NO: 54 AK018683 VDAC3 NM_005662 SEQ ID NO: 55 NP_005653.3 SEQ ID NO: 56 SLC20A2 NM_006749.3 SEQ ID NO: 57 NP_006740.1 SEQ ID NO: 58 THAP1 NM_018105 SEQ ID NO: 59 NP_060575.1 SEQ ID NO: 60 LOC441347 XM_940754 SEQ ID NO: 61 XP_945847.1 SEQ ID NO: 62 CCND1 NM_053056 OR SEQ ID NO: 63 NP_444284.1 SEQ ID NO: 64 NM_001758; FGF3 NM_005247; SEQ ID NO: 65 NP_005238.1 SEQ ID NO: 66 FADD CR456738 SEQ ID NO: 67 CAG33019.1 SEQ ID NO: 68 PPFIA1 NM_003626; SEQ ID NO: 69 NP_003617.1 SEQ ID NO: 70 CTTN NM_005231; SEQ ID NO: 71 NP_005222.2 SEQ ID NO: 72 NADSYN1 NM_018161 SEQ ID NO: 73 NP_060631.2 SEQ ID NO: 74 KRTAP5-9 NM_005553 SEQ ID NO: 75 NP_005544.4 SEQ ID NO: 76 FOLR3 NM_000804 SEQ ID NO: 77 NP_000795.2 SEQ ID NO: 78 NEU3 NM_006656.5 SEQ ID NO: 79 NP_006647.3 SEQ ID NO: 80 LHX1 NM_005568.2 SEQ ID NO: 81 NP_005559.2 SEQ ID NO: 82 DDX52 NM_007010.2 SEQ ID NO: 83 NP_008941.2 SEQ ID NO: 84 TBC1D3 NM_032258.1 SEQ ID NO: 85 NP_115634.1 SEQ ID NO: 86 SOCS7 NM_014598 XM_371052 SEQ ID NO: 87 NP_055413.1 SEQ ID NO: 88 PCGF2 NM_007144.2 SEQ ID NO: 89 NP_009075.1 SEQ ID NO: 90 PSMB3 NM_002795.2 SEQ ID NO: 91 NP_002786.2 SEQ ID NO: 92 PIP5K2B NM_003559.4 SEQ ID NO: 93 NP_003550.1 SEQ ID NO: 94 FLJ20291 AK000298.1 SEQ ID NO: 95 BAA91065 SEQ ID NO: 96 PPARBP NM_004774.2 SEQ ID NO: 97 NP_004765.2 SEQ ID NO: 98 STARD3 NM_006804.2 SEQ ID NO: 99 NP_006795.2 SEQ ID NO: 100 TCAP NM_003673.2 SEQ ID NO: 101 NP_003664.1 SEQ ID NO: 102 PERLD1 NM_033419 SEQ ID NO: 103 NP_219487.3 SEQ ID NO: 104 GRB7 NM_001030002 SEQ ID NO: 105 NP_001025173.1 SEQ ID NO: 106 GSDML NM_001042471; SEQ ID NO: 107 NP_001035936.1; SEQ ID NO: 108 NM_018530 SEQ ID NO: 109 NP_061000.2 SEQ ID NO: 110 PSMD3 NM_002809 SEQ ID NO: 111 NP_002800.2 SEQ ID NO: 112 ZNF217 NM_006526 SEQ ID NO: 113 NP_006517.1 SEQ ID NO: 114 BCAS1 NM_003657 SEQ ID NO: 115 NP_003648.1 SEQ ID NO: 116 CSTF1 NM_001033521 SEQ ID NO: 117 NP_001028693.1 SEQ ID NO: 118 RAE1 NM_003610 SEQ ID NO: 119 NP_003601.1 SEQ ID NO: 120 RNPC1 NM_017495 SEQ ID NO: 121 NP_059965.2 SEQ ID NO: 122 PCK1 AY794987 SEQ ID NO: 123 AAV50001.1 SEQ ID NO: 124 TMEPAI NM_020182 SEQ ID NO: 125 NP_064567.2 SEQ ID NO: 126 RAB22A NM_020673 SEQ ID NO: 127 NP_065724.1 SEQ ID NO: 128 VAPB NM_004738 SEQ ID NO: 129 NP_004729.1 SEQ ID NO: 130 STX16 NM_001001433 SEQ ID NO: 131 NP_001001433.1 SEQ ID NO: 132 NPEPL1 NM_024663 or SEQ ID NO: 133 NP_078939.3 SEQ ID NO: 134 NM_207402 GNAS NM_000516 SEQ ID NO: 135 NP_000507.1 SEQ ID NO: 136 TH1L NM_198976 SEQ ID NO: 137 NP_945327.1 SEQ ID NO: 138 N-PAC NM_032569 NM_018459 SEQ ID NO: 139 NP_115958.2 SEQ ID NO: 140 C20orf45 NR_003259 SEQ ID NO: 141

GO analyses of those genes showed that they are involved in aspects of nucleic acid metabolism, protein modification, signaling and the cell cycle and/or protein transport and evidence is mounting that many if not most of these genes are functionally important in the cancers in which they are amplified and over expressed (see Table 3). Indeed, published functional studies in model systems already have implicated fourteen genes in diverse aspects of cancer pathophysiology (Table 3, column 8).

Six of these are encoded in the region of amplification at 8p11. These are the RNA binding protein, LSM1 (GenBank Accession No. NM_(—)014462; SEQ ID NO:35; Fraser et al., 2005), the receptor tyrosine kinase, FGFR1 (GenBank Accession No. AY585209; SEQ ID NO: 15; Braun and Shannon, 2004), the cell cycle regulatory protein, TACC1 (GenBank Accession No. NM_(—)206862; SEQ ID NO: 43; Still et al., 1999), the metalloproteinase, ADAM9 (GenBank Accession Nos. AF495383, NM_(—)003816, NM_(—)001005845; SEQ ID NOs: 3, 5, and 7; Mazzocca et al., 2005), the serine/threonine kinase, IKBKB (GenBank Accession Nos. AY663108, NM_(—)001556, XM_(—)032491; SEQ ID NO: 19; Greten and Karin, 2004; Lam et al., 2005) and the DNA polymerase, POLB (GenBank Accession No. NM_(—)002690, SEQ ID NO: 53; Clairmont et al., 1999).

Functionally validated genes in the region of amplification at 11q13 include the cell cycle regulatory protein, CCND1 (GenBank Accession Nos. NM_(—)053056, NM_(—)001758; SEQ ID NO: 63; Hinds et al., 1994), and the growth factor, FGF3 (GenBank Accession Nos. NM_(—)005247, SEQ ID NO: 65; Okunieff et al., 2003).

Functionally important genes in the region of amplification at 17q include the transcription regulation protein, PPARBP (GenBank Accession No. NM_(—)004774.2; SEQ ID NO: 97; Zhu et al., 2000), the receptor tyrosine kinase ERBB2 (GenBank Accession No. AY208911, NM_(—)004448, NM_(—)001005862; SEQ ID NOs: 9, 11, 13; Slamon et al., 1989) and the adapter protein, GRB7 (GenBank Accession No. NM_(—)001030002, SEQ ID NO: 105; Tanaka et al., 2000).

The AKT pathway-associated-transcription factor, ZNF217 (GenBank Accession No. NM_(—)006526; SEQ ID NO: 113; Huang et al., 2005; Nonet et al., 2001) and the RNA binding protein, RAE1 (GenBank Accession No. NM_(—)003610; SEQ ID NO: 119; Babu et al., 2003) are functionally validated genes encoded in the region of amplification at 20q13.

As set forth in Table 3, column 9, further support for the functional importance of 21 of these genes (TACC1, ADAM9, IKBKB, POLB, CCND1, PCGF2, PSMB3, PIP5K2B, FLJ20291, STARD3, TCAP, PNMT, PERLD1, GRB7, GSDML, PSMD3, NR1D1, ZNF217, BCAS1, TH1L, and C20orf45) in oncogenesis comes from the observation that they are within 100 Kbp of sites of recurrent tumorigenic viral integration in the mouse (Akagi et al., 2004); in particular, three (IKBKB, CCND1, GRB7) are within 10 Kbp of such a site. Taking proximity to a site of recurrent tumorigenic viral integration as evidence for a role in cancer genesis, an additional 13 genes or transcripts are implicated (see Table 3); these are the genes that are near viral insertion sites but are: (1) not associated with outcome [highlighted gray] and (2) not previously associated to cancer [column 8].

The biological roles of the genes deregulated by recurrent high level amplification are diverse and vary between regions of amplification. For example, genes deregulated by amplification at 11q13 and 17q11-12 predominantly involved signaling and cell cycle regulation while genes deregulated by amplification at 8p11-12 and 20q13 were of mixed function but were associated most frequently with aspects of nucleic acid metabolism. The predominance of genes involved in nucleic acid metabolism in the region of amplification at 8p11-12 was especially strong.

Gene Deletion. Interestingly, the region of recurrent amplification at 8p11-12 described above was reduced in copy number in some tumors and this event also was associated with poor outcome. Thus, this is evidence that the poor clinical outcome in tumors with 8p11-12 abnormalities is due to increased genome instability/mutagenesis resulting from either up- or down-regulation of genes encoded in this region. This is supported by studies in yeast showing that up- or down-regulation of genes involved in chromosome integrity and segregation can produce similar instability phenotypes (Ouspenski et al., 1999).

Therapeutic Targets

Thus, the 66 genes we set forth in Table 3 were found to be deregulated by the high level amplifications and were associated with poor outcome; these genes and their gene products serve as therapeutic targets for cancer treatment, in particular those patients that are refractory to current therapies. Small molecule or antibody based inhibitors have already been developed for FGFR1 (PD173074, (Ray et al., 2004)), IKBKB (PS-1145; (Lam et al., 2005)) and ERBB2 (Trastuzumab, (Vogel et al., 2002)).

Six genes set forth in Table 3 (PROSC, ADAM9, FNTA, ACACA, PNMT, and NR1D1) are considered as druggable based on the presence of predicted protein folds that favor interactions with drug-like compounds (Russ and Lampel, 2005).

Taking ERBB2 as the paradigm (recurrently amplified, over expressed, associated with outcome and with demonstrated functional importance in cancer), indicates that FGFR1, TACC1, ADAM9, IKBKB, PNMT, and GRB7 are high priority therapeutic targets in these regions of amplification. Thus, it is expected that the studies and effects of inhibition on ADAM9, as described in Example 10, may be carried out and observed for any of these genes as well. Furthermore, it is contemplated that antagonists of these genes can be made by one having skill in the art, including but not limited to, inhibitory oligonucleotides and peptides, aptamers, small molecules, drugs and antibodies, thereby producing an effect on the gene or gene product as a treatment for breast cancer.

Molecular Characteristics and Associations.

We assessed genome copy number using BAC array CGH (Hodgson et al., 2001; Pinkel et al., 1998; Snijders et al., 2001; Solinas-Toldo et al., 1997) and gene expression profiles using Affymetrix U133A arrays (Ramaswamy et al., 2003; Reyal et al., 2005) in breast tumors from a cohort of patients treated according to the standard of care between 1989 and 1997 (surgery, radiation, hormonal therapy and treatment with high dose adriamycin and cytoxan as indicated). We measured genome copy number profiles for 145 primary breast tumors and gene expression profiles for 130 primary tumors, of which 101 were in common. We analyzed these data to identify recurrent genomic and transcriptional abnormalities and we assessed associations with clinical endpoints to identify genomic events that might contribute to cancer pathophysiology.

Genome copy number and gene expression features. We found that the recurrent genome copy number and gene expression characteristics measured for the patient cohort in this study were similar to those reported in earlier studies. We summarize these briefly.

FIG. 1( a) and FIG. 1( b) show numerous regions of recurrent genome CNA and 9 regions, as shown in Table 1, of recurrent high level amplification involving regions of chromosomes 8, 11, 12, 17 and 20 while FIG. 2 shows that analysis of these data using unsupervised hierarchical clustering resolves these tumors into the “1q/16q” (or “simple”), “complex” and “amplifier” genome aberration subtypes (Fridlyand et al., 2006). The genomic extents of the regions of amplification are listed in Table 1. These were generally similar to those reported in earlier studies using chromosome (Kallioniemi et al., 1994) and array CGH (Loo et al., 2004; Naylor et al., 2005; Pollack et al., 1999; Pollack et al., 2002). Several of these regions of amplification were frequently co-amplified. Declaring a Fisher exact test p-value of less than 0.05 for pair-wise associations to be suggestive of possible significant co-amplification, we found co-amplification of 8q24 and 20q13 and co-amplification of regions at 11q13-14, 12q13-14, 17q11-12, and 17q21-24. These analyses were underpowered to achieve significance with proper correction for multiple testing so these associations are suggestive but not significant. However, these associations were consistent with the report of Al Kuraya et al (Al-Kuraya et al., 2004) who showed evidence for co-amplification of genes in several of these regions of amplification including ERBB2, MYC, CCND1 and MDM2 and that of Naylor et al (Naylor et al., 2005) showing co-amplification of 17q12 and 17q25.

FIG. 6 shows that unsupervised hierarchical clustering of intrinsically variable genes resolves the tumors in our study cohort into the luminal A, luminal B, basal-like and ERBB2 expression subtypes previously reported for breast tumors (Perou et al., 1999; Perou et al., 2000; Sorlie et al., 2003). We assessed the genomic characteristics of these expression subtypes in subsequent analyses.

Associations between CNAs and expression. Combined analyses of genome copy number and expression showed that the recurrent genome CNAs differed between expression subtypes and identified genes whose expression levels were significantly deregulated by the CNAs. FIGS. 1( c)-1(j) show the recurrent CNAs for each expression subtype. In these analyses, we assigned each tumor to the expression subtype cluster (basal-like, ERBB2, luminal A, and luminal B) to which its expression profile was most highly correlated. We did not assess aberration in normal-like tumors due to the small number of such tumors.

FIG. 1( c) shows that the basal-like tumors were relatively enriched for low-level copy number gains involving 3q, 8q, and 10p and losses involving 3p, 4p, 4q, 5q, 12q, 13q, 14q and 15q while FIG. 1( d) shows that high level amplification at any locus was infrequent in these tumors. FIG. 1( e) shows that ERBB2 tumors were relatively enriched for increased copy number at 1q, 7p, 8q, 16p and 20q and reduced copy number at 1p, 8p, 13q and 18q. FIG. 1( f) shows that amplification of ERBB2 was highest in the ERBB2 subtype as expected but amplification of noncontiguous, distal regions of 17q also was frequent as previously reported (Barlund et al., 1997). FIG. 1( g) shows that increased copy number at 1q and 16p and reduced copy number at 16q were the most frequent abnormalities in luminal A tumors while FIG. 1( h) shows that amplifications at 8p11-12, 11q13-14, 12q13-14, 17q11-12, 17q21-24 and 20q13 were relatively common in this subtype. FIG. 1( i) shows that gains of chromosomes 1q, 8q, 17q and 20q and losses involving portions of 1p, 8p, 13q, 16q, 17p and 22q were prevalent in luminal B tumors while FIG. 1( j) shows that high level amplifications involving 8p11-12, two regions of 8q, and 11q13-14 were frequent.

In order to understand how the genome aberrations were influencing cancer pathophysiologies, we identified genes that were deregulated by recurrent genome CNAs. We took these genes to be those whose expression levels were significantly associated with copy number (Holm-adjusted p-value <0.05). These genes, which represent about 10% of the genome interrogated by the Affymetrix HGU133A arrays used in this study, and their copy number-expression level correlation coefficients are listed in Table 4 This extent of genome-aberration-driven deregulation of gene expression is similar to that reported in earlier studies (Hyman et al., 2002; Pollack et al., 1999).

We tested associations between copy number and expression level for 186 genes in regions of amplification at 8p11-12, 11q13-q14, 17q11-12 and 20q13 (see Table 5) and we identified 66 genes in these regions whose expression levels were correlated with copy number (FDR<0.01, wilcoxon rank sum test; Table 3). These genes define the transcriptionally important extents of the regions of recurrent amplification. Twenty-three were from a 5.5 Mbp region at 8p11-12 flanked by SPFH2 and LOC441347, ten were from a 6.6 Mbp region at 11q13-14 flanked by CCND1 and PRKRIR, nineteen were from a 3.1 Mbp region at 17q12 flanked by LHX1 and NR1D1 and fourteen were from a 5.4 Mbp region at 20q13 flanked by ZNF217 and C20orf45.

Since the recurrent genome aberrations differed between expression subtypes, we explored the extent to which the expression subtypes were determined by genome copy number. Specifically, we applied unsupervised hierarchical clustering to intrinsically variable genes after removing genes whose expression levels were correlated with copy number. FIG. 4 shows that the tumors still resolve into the basal-like and luminal classes. However, the ERBB2 cluster was lost.

Associations with Clinical Variables.

Associations with histopathology. FIG. 2 and Table 2 summarize associations of histopathological features with aspects of genome abnormality including recurrent genome abnormalities, total number of copy number transitions, fraction of the genome altered (FGA), number of chromosomal arms containing at least one amplification, number of recurrent amplicons and presence of at least one recurrent amplification.

These analyses showed that ER/PR negative tumors were predominantly found in the basal-like and “complex” expression and genome aberration subtypes, respectively. Node-positive tumors had significantly more amplified arms and recurrent amplicons than node-negative samples but showed a much more moderate difference in terms of low-level copy number transitions. Stage 1 tumors had moderately fewer low- and high-level changes than higher stage tumors. The number of low and high level abnormalities increased with SBR grade. Interestingly, the “complex” tumors showing many low-level abnormalities were more strongly associated with aberrant p53 expression than “amplifying” tumors. “Simple” tumors tended to have Ki67 proliferation indices<10% while “complex” and “amplifying” tumors typically had Ki67 indices>10%. The number of amplifications increased significantly with tumor size but the number of low level changes did not. We observed no association of genomic changes with the age at diagnosis.

Associations with outcome. FIG. 4 and Table 5 summarize associations between histopathological, transcriptional and genomic characteristics and outcome endpoints identified using multivariate regression analysis. Histopathological features including size and nodal status were significantly associated with survival duration and/or disease recurrence in univariate analyses (Table 4) and were included in the multivariate regressions described below.

The tumor subtypes based on patterns of gene expression or genome aberration content showed moderate associations with outcome endpoints. For example, FIG. 3( a) shows that patients with tumors classified as ERBB2 based on expression pattern had significantly shorter disease-specific survival than patients classified as luminal A, luminal B, or normal-like as previously reported (Perou et al., 2000; Sorlie et al., 2001). Unlike these earlier reports, patients with tumors classified as basal-like did not do significantly worse than patients with luminal or normal breast-like tumors although there was a trend in that direction. In addition, FIG. 3( b) indicates that patients with tumors classified as “1q/16q” based on genome aberration content tended to have longer disease-specific survival than patients with “complex” or “amplifier” tumors.

We found that high level amplification was most strongly associated with poor outcome in this aggressively treated patient population. Amplification at any of the 9 recurrent amplicons was an independent risk factor for reduced survival duration (p<0.04) and distant recurrence (p<0.01) in a multivariate Cox-proportional model that included tumor size and nodal status. FIG. 3( c), for example, shows that patients whose tumors had at least one recurrent amplicon survived a significantly shorter time than did patients with tumors showing no amplifications. More specifically, amplifications of 8p11-12 or 17q11-12 (ERBB2) were significantly associated with disease-specific survival and distant recurrence in all patients in multivariate regressions (Table 1).

Importantly, we found that stratification according to amplification status allowed identification of patients with poor outcome even within an expression subtype. FIG. 3( d), for example, shows that patients with luminal A tumors and amplification at 8p11-12, 11q13-14 or 20q13 had significantly shorter disease-specific survival than patients without amplification in one of these regions (the number of samples in the luminal A subtype group was too small for multivariate regressions). Amplification at 8p11-12 was most strongly associated with distant recurrence in the luminal A subtype.

Considering the strong association between amplification and outcome, we explored the possibility that some of these genes were over expressed in tumors in which they were not amplified and that over expression was associated with reduced survival duration in those tumors. Increased expression levels of 7 genes are labeled in Table 3 in dark gray (CTTN, KRTAP5-9, LHX1, PPARBP, PNMT, GRB7, TMEPAI). These genes were associated with reduced survival or distant recurrence at the p<0.1 level but only two, the growth factor receptor binding protein, GRB7 (17q) and the keratin associated protein, KTRAP5-9 (11q), at the p<0.05 level.

Interestingly, this expression analysis also revealed an unexpected association between reduced expression levels of genes from regions of amplification and poor outcome (either disease free survival or distant recurrence) in tumors without relevant amplifications (p<0.05). This was especially prominent for genes from the region of amplification at 8p11-12 (14 of 23 genes in this region showed this association) while only two genes from regions of adverse-outcome-associated amplifications on chromosomes 17q and 20q showed this association.

Following this lead, we tested associations between outcome and reduced copy number at 8p11-12 in patients in tumors in which 8p11-12 was not amplified. FIG. 3( e) shows that patients with reduced copy number at 8p11-12 did worse than patients without a deletion in this region. FIG. 3( f) shows that patients in the overall study with high level amplification or deletion at 8p11-12 survived significantly shorter survival (p=0.0017) than patients without either of those events.

We also tested for associations of low level genome copy number changes with the outcome endpoints. The most frequent low-level copy number changes (e.g. increased copy number at 1q, 8q and 20q or decreased copy number at 16q) were not significantly associated with outcome endpoints. However, we did find a significant association of the loss of a small region on 9q22 with adverse outcome, both disease-specific survival and distal recurrence, which persisted even after correction for multiple testing (p<0.05, multivariate Cox regression). This region is defined by BACs, CTB-172A10 and RP11-80F13. We also found a marginally significant association between fraction of the genome lost and disease-specific survival in luminal A tumors (p<0.02 and <0.06 for univariate and multivariate regression, respectively, Wilcoxon rank-sum test).

The lack of association of the most frequent low level CNAs with outcome raised the issue of selection pressure during tumor evolution. To understand this, we used the program GoStat (Beissbarth and Speed, 2004) to identify the Gene Ontology (GO) classes of 1444 unique genes (1734 probe sets) whose expression levels were preferentially modulated by low-level CNAs compared to 3026 probe sets whose expression levels did not show associations with copy number. The GO categories most significantly overrepresented in the set of genes with a dosage effect compared to genes with no or minimal dosage effect involved RNA processing (Holm adjusted p-value<0.001), RNA metabolism (p<0.01) and cellular metabolism (p<0.02).

EXAMPLES Example 1 Tumor Characteristics

Frozen tissue from UC San Francisco and the California Pacific Medical Center collected between 1989 and 1997 was used for this study. Tissues were collected under IRB approved protocols with patient consent. Tissues were collected, frozen over dry ice within 20 minutes of resection, and stored at −80° C. An H&E section of each tumor sample was reviewed, and the frozen block was manually trimmed to remove normal and necrotic tissue from the periphery. Clinical follow-up was available with a median time of 6.6 years overall and 8 years for censored patients. Tumors were predominantly early stage (83% stage I & II) with an average diameter of 2.6 cm. About half of the tumors were node positive, 67% were estrogen receptor positive, 60% received tamoxifen and half received adjuvant chemotherapy (typically adriamycin and cytoxan). Clinical characteristics of the individual tumors are provided together with expression and array CGH profiles in the CaBIG repository and at http://graylabdata.lbl.gov.

Example 2 Array CGH

Each sample, such as from Example 1, was analyzed using Scanning and OncoBAC arrays. Scanning arrays were comprised of 2464 BACs selected at approximately megabase intervals along the genome as described previously (Hodgson et al., 2001; Snijders et al., 2001). OncoBAC arrays were comprised of 1860 PI, PAC, or BAC clones. About three-quarters of the clones on the OncoBAC arrays contained genes and STSs implicated in cancer development or progression. All clones were printed in quadruplicate. DNA samples for array CGH were labeled generally as described previously (Hackett et al., 2003; Hodgson et al., 2001; Snijders et al., 2001). Briefly, 500 ng each of cancer and normal female genomic DNA sample was labeled by random priming with CY3- and CY5-dUTP, respectively; denatured; and hybridized with unlabeled Cot-1 DNA to CGH arrays. After hybridization, the slides were washed and imaged using a 16-bit CCD camera through CY3, CY5, and DAPI filters (Pinkel et al., 1998).

Statistical considerations. Data processing. Array CGH data image analyses were performed as described previously (Jain et al., 2002). In this process, an array probe was assigned a missing value for an array if there were fewer than 2 valid replicates or the standard deviation of the replicates exceeded 0.2. Array probes missing in more than 50% of samples in OncoBAC or Scanning array datasets were excluded in the corresponding set. Array probes representing the same DNA sequence were averaged within each dataset and then between the two datasets. Finally, the two datasets were combined and the array probes missing in more than 25% of the samples, unmapped array probes and probes mapped to chromosome Y were eliminated. The final dataset contained 2149 unique probes.

Example 3 Expression Profiling Using the Affymetrix High Throughput Analysis (HTA) System

Expression array analysis using the GeneChip® assay is implemented on the Affymetrix HTA system in four automated procedures; target preparation, hybridization, washing/staining and scanning.

Target preparation. For each sample, the RNA target is prepared by putting 2.5 g of total RNA in 5 μl water and 5 μl of 10 μM T7(dt)24 primer into a MJ Research 96-well reaction plate. The total RNA undergoes an annealing step at 70° C. for 10 minutes followed by a 4° C. cooling step for 5 minutes. The plate is transferred back to the deck position and undergoes first strand cDNA synthesis. 10 μl of First Strand Cdna Synthesis cocktail (4 μl of Affymetrix 5×1 st strand buffer (250 mM Tris-HCl, pH 8.3 at room temperature; 375 mM KCl; 15 mM MgCl2), is mixed with 2 μl 0.1M DTT, 1 μl 10 mM dNTP mix, 1 μl Superscript II (200U/ul), and 2 μl nuclease free water per reaction) is added, and the plate is then transferred to the thermal cycler and incubated at 42° C. for 60 minutes and 4° C. for 5 min. 91 μl of nuclease free water and 39 μl of the Second Strand cDNA Synthesis cocktail (30 μl of Affymetrix 5×2nd strand buffer, 100 mM Tris-HCl (pH 6.9), 23 mM MgCl2, 450 mM KCl, 0.75 mM β-NAD, 50 mM (NH4)2SO4); 3 μl 10 mM dNTP; 1 μl 10 unit/μl DNA Ligase; 4 μl 10 unit/μl DNA Polymerase and 1 μl 2 units/μl RNase H) is added. The plate is incubated at 16° C. for 120 minutes and 4° C. for 5 minutes. 4 μl of T4 Polymerase cocktail comprised of 2 μl T4 DNA Polymerase plus 2 μl 1× T4 DNA Polymerase Buffer (165 mM Tris-acetate (pH 7.9), 330 mM Sodium-acetate, 50 mM Magnesium-acetate, 5 mM DTT) is added and the plate is taken back to the thermal cycler where it is cycled at 16° C. for 10 minutes, 72° C. for 10 minutes, and cooled to 4° C. for 5 minutes.

The plate is transferred back to the deck and Agencourt Magnetic Beads are used for the cDNA clean-up. 162 μl of magnetic beads are mixed with 90 μl of in the cDNA Clean-Up Plate and incubated for 5 minutes. Post incubation, the cDNA bound to the beads in the cDNA Clean-Up Plate is moved to the Agencourt magnetic plate. Another 115 μl of magnetic beads is mixed with 64 μl cDNA incubated for 5 minutes, and then moved to the Agencourt magnetic plate. Post incubation, the supernatant is removed and two washes with 75% EtOH are performed using 200 μl solution. The EtOH is then removed and the beads sit for 5 minutes. 40 μl of nuclease free water is added to the beads and mixed well. The solution is then incubated for 1 minute, and then it is taken back to the magnetic plate where it is incubated for 5 minutes to capture the beads on the magnet. 22 μl of eluted cDNA is then transferred to the Purified cDNA Plate (22 μl total volume). 38 μl of IVT cocktail (6 μl 10× IVT Buffer, 18 μl HTA RLR Reagent (labeling NTP), 6 μl HTA Enzyme Mix, 1 μl T7 RNA Polymerase, and 7 μl RNase free water per reaction is added to the purified cDNA) is added to the 22 μl of purified cDNA (60 μl total volume). The plate is then transferred to the thermal cycler where incubation of 8 hours at 37° C. occurs.

Upon completion, the plate is transferred back to the deck where 120 μL Agencourt Magnetic Beads are used to clean up the cRNA product. The A260 of the purified cRNA is measured in a plate spectrophotometer, then the concentration in each well of a 96 well plate is adjusted to a calculated value of 0.625 μg/μl. A second reading is taken to verify the normalization process. 30 μl of cRNA was transferred from the cRNA Normalization Plate and dispensed in the Fragmented cRNA Plate. 7.5 μl of 5× fragmentation buffer per sample is added. The plate is then transferred to the thermal cycler where it is held at 94° C. for 35 minutes followed by a cooling step at 20° C. for 5 minutes. The sample is then mixed with 90 μl of hybridization cocktail (3 μl of 20× bioB, bioC, bioD, and creX hybridization controls mixed with 1.6 μl 3 nM oligo-B2, 1 μl 10 mg/ml Herring sperm DNA, 1 μl 50 mg/ml acetylated BSA, and 83.4 μl 1.2× Hybridization Buffer).

Hybridization. The sample is then ready to be hybridized. The peg array plate is incubated in 60 μl pre-hybridization cocktail (1 μl 10 mg/ml Herring sperm DNA, 1 μl 150 mg/ml Acetylated BSA, 84 μl Hybridization buffer, 15 μl nuclease free H20 per reaction). The hybridization-ready sample is taken to the thermal cycler and denatured for 95° C. for 5 minutes. Upon completion of this step, the plate is returned to the deck where 70 μl of sample is transferred to a hybridization tray. The peg plate is then lifted off of the pre-hybridization tray and taken to the hybridization plate where it is placed. This “hybridization sandwich” is then manually transferred to a hybridization oven where it incubates at 48° C. for 16-18 hours. Washing/Staining. The robot lifts the peg plate off of the hybridization tray and transfers it to the first low stringency wash (LSW) (6×SSPE, 0.01% Tween-20) where it is dipwashed 36 times. The plate is then transferred to the other three low stringency wash positions where the dipping is repeated. The peg plate is then moved to the high stringency wash (HSW) (100 mM MES, 0.1M NaCl, 0.01% Tween-20) where it is incubated at 41° C. for 25 minutes. After the incubation, the peg plate is transferred to a fifth LSW tray where the HSW removed by rinsing. The plate is transferred to the first stain (31.5 μl nuclease free H20, 35 μl 2×MES stain buffer, 2.8 μl 50 mg/ml Acetylated BSA, 0.7 μl R-Phycoerythrin Streptavidin), where it incubates at room temperature for 10 minutes. At the end of the 10 minute incubation, the peg plate undergoes another 4 cycles of dip washing method. The peg tray is then transferred to stain 2 (2.8 μl 50 mg/ml Acetylated BSA, 0.7 μl reagent grade goat IgG, 0.4 μl biotinylated goat Anti-streptavidin antibody per reaction). The above method is repeated for stain 3 (31.5 μl nuclease free H20, 35 μl 2×MES stain buffer, 2.8 μl 50 mg/ml Acetylated BSA, 0.7 μl R-Phycoerythrin Streptavidin). At the end of the incubation of the third stain, the peg plate is washed 36 times in LSW. The robot then transfers 70 μl of MES holding buffer, 68 mM MES, 1.0 M NaCl, 0.01% Tween-20, into a sterile scan tray. The peg tray is then placed into the scan tray for scanning.

Scanning. The 96 well peg plate is scanned by the Affymetrix High Throughput (HT) scanner, a fully automated epi-fluorescence imaging system with an excitation wavelength range of 340 nm to 675 nm and a cooled 1280×1024 CCD camera with 12 bit readout. Scanning resolution is 1.0 μm/pixel with a 10× objective. Images are captured at two different exposure times. Each well will have 49 sub-images/exposure times. The software program then converts these .dat files into mini .cel files and then into composite cel files where the information is analyzed in the Affymetrix GCOS 1.2 software.

Statistical considerations. Data processing. For Affymetrix data, multi-chip robust normalization was performed using RMA software (Irizarry et al., 2003). Transcripts assessed on the arrays were classified into two groups using Gaussian model-based clustering by considering the joint distribution of the median and standard deviation of each probe set across samples. During this process, computational demands were reduced by randomly sampling and clustering 2000 probe intensities using melust (Yeung et al., 2001; Yeung et al., 2004) with two clusters and unequal variance. Next, the remaining probe intensities were classified into the newly created clusters using linear discriminant analysis. The cluster containing probe intensities with smaller mean and variance was defined as “not expressed” and the second cluster was “expressed”.

Example 4 Assessment of Genome Copy Numbers

Characterizing copy number changes. The array CGH data were analyzed using circular binary segmentation (CBS) (Olshen et al., 2004) to translate intensity measurements into regions of equal copy number as implemented in the DNA copy R/Bioconductor package. Missing values for probes mapping within segmented regions of equal copy number were imputed by using the value of the corresponding segment. A few probes with missing values (<0.3%) were located between segmented regions and their values were imputed using the maximum value of the two flanking segments. Thus, each probe was assigned a segment value referred to as its “smoothed” value. The scaled median absolute deviation (MAD) of the difference between the observed and smoothed values was used to estimate the tumor-specific experimental variation. All tumors had noise standard deviation of less than 0.2. The gain and loss status for each probe was assigned using the merge Level procedure as described (Willenbrock and Fridlyand, 2005). In this process, segmental values across the genome were merged to create a common set of copy number levels for each individual tumor. The probes corresponding to the copy number level with the smallest absolute median value were declared unchanged whereas all the other probes were either gained or lost depending on the sign of the segment mean. Additionally, to account for high level focal aberrations being single outliers and thus assigned the status of the surrounding segments, the probe was assigned gain status when amplified as described below.

The frequency of alterations at each probe locus was computed as the proportion of samples showing an aberration at that locus. The genome distance assigned to each probe was computed by assigning a genomic distance equal to half the distance to the neighboring probes or to the end of a chromosome for the probes with only one neighbor. The number of copy number transitions was computed based on the initial DNA copy segmentation by counting the number of copy number transitions in the genome (Snijders et al., 2003). Single outliers such as high level amplifications were identified by assigning the original observed log2ratio to the probes for which the observed values were more than 4 tumor-specific MAD away from the smoothed values. The amplification status for a probe was then determined by considering the width of the segment to which that probe belonged (0 if an outlier) and a minimum difference between the smoothed value of the probe (observed value if an outlier) and the segment means of the neighboring segments. The clone was declared amplified if it belonged to the segment spanning less than 20 Mb and the minimum difference was greater than exp(−x³) where x is the final smoothed value for the clone. Note that this allowed clones with small log2ratio to be declared amplified if they were high relative to the surrounding clones with the required difference becoming larger as value of the clone gets smaller (e.g. a difference of 1 was required when clone value was 0 and 0.36 when the clone value was 1; Albertson, Fridlyand, private communication).

Clustering of genome copy number profiles. Genome copy number profiles were clustered using smoothed imputed data with outliers present. Agglomerative hierarchical clustering with Pearson correlation as a similarity measure and the Ward method to minimize sum of variances were used to produce compact spherical clusters (Hartigan, 1975). The number of groups was assessed qualitatively by considering the shape of the clustering dendogram.

Expression subtype assignment. Tumors were classified according to expression phenotype (basal, ERBB2, luminal A, luminal B and normal-like) by assigning each tumor to the subtype of the cluster defined by hierarchical clustering of expression profiles for 122 samples published by Sorlie et al (Sorlie et al., 2003) to which it had the highest Pearson correlation. The correlation was computed using the subset of Stanford intrinsically variable genes common to both datasets. Unigene IDs were used to match the probes and genes with non-unique Unigene IDs. These data were averaged and the genes were median-centered for both datasets. For robustness, only 79 of the most tightly clustered Stanford samples were used to define Stanford cluster centroids. Unigene IDs for Affymetrix data were obtained from the TIGR Resourcer website, http://pga.tigr.org/tigr-scripts/magic/rl.pl. The Stanford intrinsic genes list was downloaded from http://genome-www.stanford.edu/breast_cancer/robustness/data.shtml. The same procedure was used to assign expression subtypes to the 295 breast tumors dataset published by van de Vijver et al., (van de Vijver et al., 2002) downloaded from http://www.rii.com/publications/2002/default.html except that matching was done directly using gene names.

Association of copy number with survival. Stage 4 samples were excluded from all the outcome-related analyses; and disease-specific survival and time to distant recurrence were used as the two endpoints. We identified clinical variables independently associated with outcome endpoints by first using univariate Cox-proportional hazards model to identify clinical variables individually associated with the outcomes and then identifying the subset of variables significant in the additive multivariate model which included all significant variables from univariate analyses. Significance was declared at the 0.05 level. As demonstrated in (Willenbrock and Fridlyand, 2005), analyzing segmented data greatly increases power to detect true significant associations without increasing the false positive rate. Therefore, we used smoothed imputed data with outliers as described above to identify significant associations of low-level copy number changes with outcome endpoints. P-values were adjusted using False Discovery Rate (FDR) and a genome association was considered significant if its FDR was less than 0.05. A Cox proportional model also was used to associate the total number of copy number transitions and amount of genome gained and lost with survival; overall and within expression subtypes. P-values were not adjusted for FDR for these two analyses due to their targeted nature and significance was declared at the 0.05 level.

Regions of high level amplification were declared recurrent when present in at least 5 samples. The BAC array probes were further manually grouped to form groups of contiguous regions thereby referred to as amplicons, and singletons were excluded. Each sample was further classified as amplified for a given amplicon if it contained at least one amplified probe in the amplicon region. We tested all amplicons for association with the outcome variables by fitting univariate and multivariate Cox-proportional models with and without clinical variables and assessing significance of the standardized Cox-proportional coefficient. Significance was declared at unadjusted p-value<0.05.

Association of copy number with expression. The presence of an overall dosage effect was assessed by subdividing each chromosomal arm into non-overlapping 20 Mb bins and computing the average of cross-Pearson-correlations for all gene transcript-BAC probe pairs that mapped to that bin. We also calculated Pearson correlations and corresponding p-values between expression level and copy number for each gene transcript. Each transcript was assigned an observed copy number of the nearest mapped BAC array probe. 80% of gene transcripts had a nearest clone within 1 Mbp and 50% had a clone within 400 kbp. Correlation between expression and copy number was only computed for the gene transcripts whose absolute assigned copy number exceeded 0.2 in at least 5 samples. This was done to avoid spurious correlations in the absence of real copy number changes. We used conservative Holm p-value adjustment to correct for multiple testing. Gene transcripts with an adjusted p-value<0.05 were considered to have expression levels that were highly significantly affected by gene dosage. This corresponded to a minimum Pearson correlation of 0.44.

Associations of transcription and CNA in regions of amplification with outcome in tumors without particular amplicons. We assessed the associations of levels of transcripts in regions of amplifications with survival or distant recurrence in tumors without amplifications in order to find genes that might contribute to progression when deregulated by mechanisms other than amplification (e.g. we assessed associations between expression levels of the genes mapping to the 8p11-12 amplicon and survival in samples without 8p11-12 amplification. We performed separate cox-proportional regressions for disease-specific survival and distant recurrence. Stage 4 samples were excluded from all analyses. Testing for functional enrichment. We used the gene ontology statistics tool, GoStat (Beissbarth and Speed, 2004) to test whether gene transcripts with strongest dosage effects were enriched for particular functional groups. The p-values were adjusted using False Discovery Rate. The categories were considered significantly overrepresented if the FDR-adjusted p-value was less than 0.001. Since expressed genes were significantly more likely to show dosage effects than non expressed genes (p-value<2.2e-16, Wilcoxon rank sum test), GoStat comparisons were performed only for expressed genes. Specifically, GO categories for 1734 expressed probes with significant dosage effect (Holm p-value<0.05) were compared with those for 3026 expressed probes with no dosage effect (Pearson correlation<0.1).

Example 5 Probe Preparation

Methods of preparing probes are well known to those of skill in the art (see, e.g. Sambrook et al., Molecular Cloning: A Laboratory Manual (2nd ed.), Vols. 1-3, Cold Spring Harbor Laboratory, (1989) or Current Protocols in Molecular Biology, F. Ausubel et al., ed. Greene Publishing and Wiley-Interscience, New York (1987)), which are hereby incorporated by reference.

Prior to use, constructs are fragmented to provide smaller nucleic acid fragments that easily penetrate the cell and hybridize to the target nucleic acid. Fragmentation can be by any of a number of methods well known to hose of skill in the art. Preferred methods include treatment with a restriction enzyme to selectively cleave the molecules, or alternatively to briefly heat the nucleic acids in the presence of Mg²⁺. Probes are preferably fragmented to an average fragment length ranging from about 50 bp to about 2000 bp, more preferably from about 100 bp to about 1000 bp and most preferably from about 150 bp to about 500 bp.

Methods of labeling nucleic acids are well known to those of skill in the art. Preferred labels are those that are suitable for use with in situ hybridization. The nucleic acid probes may be detectably labeled prior to the hybridization reaction. Alternatively, a detectable label which binds to the hybridization product may be used. Such detectable labels include any material having a detectable physical or chemical property and have been well-developed in the field of immunoassays.

As used herein, a “label” is any composition detectable by spectroscopic, photochemical, biochemical, immunochemical, or chemical means. Useful labels in the present invention include radioactive labels (e.g, ³²P, ¹²⁵I, ¹⁴C, ³H, and ³⁵S), fluorescent dyes (e.g. fluorescein, rhodamine, Texas Red, etc.), electron-dense reagents (e.g. gold), enzymes (as commonly used in an ELISA), calorimetric labels (e.g. colloidal gold), magnetic labels (e.g. DYNABEADS™), and the like. Examples of labels which are not directly detected but are detected through the use of directly detectable label include biotin and dioxigenin as well as haptens and proteins for which labeled antisera or monoclonal antibodies are available.

The particular label used is not critical to the present invention, so long as it does not interfere with the in situ hybridization of the stain. However, stains directly labeled with fluorescent labels (e.g. fluorescein-12-dUTP, Texas Red-5-dUTP, etc.) are preferred for chromosome hybridization.

A direct labeled probe, as used herein, is a probe to which a detectable label is attached. Because the direct label is already attached to the probe, no subsequent steps are required to associate the probe with the detectable label. In contrast, an indirect labeled probe is one which bears a moiety to which a detectable label is subsequently bound, typically after the probe is hybridized with the target nucleic acid.

In addition the label must be detectable in as low copy number as possible thereby maximizing the sensitivity of the assay and yet be detectible above any background signal. Finally, a label must be chosen that provides a highly localized signal thereby providing a high degree of spatial resolution when physically mapping the stain against the chromosome. Particularly preferred fluorescent labels include fluorescein-12-dUTP and Texas Red-5-dUTP.

The labels may be coupled to the probes in a variety of means known to those of skill in the art. In a preferred embodiment the nucleic acid probes will be labeled using nick translation or random primer extension (Rigby, et al. J. Mol. Biol., 113: 237 (1977) or Sambrook, et al., Molecular Cloning—A Laboratory Manual, Cold Spring Harbor Laboratory, Cold Spring Harbor, N.Y. (1985)).

One of skill in the art will appreciate that the probes of this invention need not be absolutely specific for the targeted 8p11-12, 11q13-14, 17q11-12 or 20q13 regions of the genome. Rather, the probes are intended to produce “staining contrast”. “Contrast” is quantified by the ratio of the probe intensity of the target region of the genome to that of the other portions of the genome. For example, a DNA library produced by cloning a particular chromosome (e.g. chromosome 7) can be used as a stain capable of staining the entire chromosome. The library contains both sequences found only on that chromosome, and sequences shared with other chromosomes. Roughly half the chromosomal DNA falls into each class. If hybridization of the whole library were capable of saturating all of the binding sites on the target chromosome, the target chromosome would be twice as bright (contrast ratio of 2) as the other chromosomes since it would contain signal from the both the specific and the shared sequences in the stain, whereas the other chromosomes would only be stained by the shared sequences. Thus, only a modest decrease in hybridization of the shared sequences in the stain would substantially enhance the contrast. Thus, contaminating sequences which only hybridize to non-targeted sequences, for example, impurities in a library can be tolerated in the stain to the extent that the sequences do not reduce the staining contrast below useful levels.

Example 6 In Situ Hybridization

Generally, in situ hybridization comprises the following major steps: (1) fixation of tissue or biological structure to analyzed; (2) prehybridization treatment of the biological structure to increase accessibility of target DNA, and to reduce nonspecific binding; (3) hybridization of the mixture of nucleic acids to the nucleic acid in the biological structure or tissue; (4) posthybridization washes to remove nucleic acid fragments not bound in the hybridization and (5) detection of the hybridized nucleic acid fragments. The reagents used in each of these steps and their conditions for use vary depending on the particular application.

In some applications it is necessary to block the hybridization capacity of repetitive sequences. In this case, human genomic DNA is used as an agent to block such hybridization. The preferred size range is from about 200 bp to about 1000 bases, more preferably between about 400 to about 800 bp for double stranded, nick translated nucleic acids.

Hybridization protocols for the particular applications disclosed here are described in Pinkel et al. Proc. Natl. Acad. Sci. USA, 85: 9138-9142 (1988) and in EPO Pub. No. 430,402. Suitable hybridization protocols can also be found in Methods in Molecular Biology Vol. 33, In Situ Hybridization Protocols, K. H. A. Choo, ed., Humana Press, Totowa, N.J., (1994). +In a particularly preferred embodiment, the hybridization protocol of Kallioniemi et al., ERBB2 amplification in breast cancer analyzed by fluorescence in situ hybridization. Proc Natl Acad Sci USA, 89: 5321-5325 (1992) is used.

Typically, it is desirable to use dual color FISH, in which two probes are utilized, each labeled by a different fluorescent dye. A test probe that hybridizes to the region of interest is labeled with one dye, and a control probe that hybridizes to a different region is labeled with a second dye. A nucleic acid that hybridizes to a stable portion of the chromosome of interest, such as the centromere region, is often most useful as the control probe. In this way, differences between efficiency of hybridization from sample to sample can be accounted for.

The FISH methods for detecting chromosomal abnormalities can be performed on nanogram quantities of the subject nucleic acids. Paraffin embedded tumor sections can be used, as can fresh or frozen material. Because FISH can be applied to the limited material, touch preparations prepared from uncultured primary tumors can also be used (see, e.g., Kallioniemi, A. et al., Cytogenet. Cell Genet. 60: 190-193 (1992)). For instance, small biopsy tissue samples from tumors can be used for touch preparations (see, e.g., Kallioniemi, A. et al., Cytogenet. Cell Genet. 60: 190-193 (1992)). Small numbers of cells obtained from aspiration biopsy or cells in bodily fluids (e.g., blood, urine, sputum and the like) can also be analyzed. For prenatal diagnosis, appropriate samples will include amniotic fluid and the like.

Example 7 Quantitative PCR

Elevated gene expression is detected using quantitative PCR. Primers can be created to detect sequence amplification by signal amplification in gel electrophoresis. As is known in the art, primers or oligonucleotides are generally 15-40 bp in length, and usually flank unique sequence that can be amplified by methods such as polymerase chain reaction (PCR) or reverse transcriptase PCR (RT-PCR, also known as real-time PCR). Methods for RT-PCR and its optimization are known in the art. An example is the PROMEGA PCR Protocols and Guides, found at URL:<http://www.promega.com/guides/pcr guide/default.htm>, and hereby incorporated by reference. Currently at least four different chemistries, TaqMan® (Applied Biosystems, Foster City, Calif., USA), Molecular Beacons, Scorpions® and SYBR® Green (Molecular Probes), are available for real-time PCR. All of these chemistries allow detection of PCR products via the generation of a fluorescent signal. TaqMan probes, Molecular Beacons and Scorpions depend on Förster Resonance Energy Transfer (FRET) to generate the fluorescence signal via the coupling of a fluorogenic dye molecule and a quencher moiety to the same or different oligonucleotide substrates. SYBR Green is a fluorogenic dye that exhibits little fluorescence when in solution, but emits a strong fluorescent signal upon binding to double-stranded DNA.

Two strategies are commonly employed to quantify the results obtained by real-time RT-PCR; the standard curve method and the comparative threshold method. In this method, a standard curve is first constructed from an RNA of known concentration. This curve is then used as a reference standard for extrapolating quantitative information for mRNA targets of unknown concentrations. Another quantitation approach is termed the comparative C_(t) method. This involves comparing the C_(t) values of the samples of interest with a control or calibrator such as a non-treated sample or RNA from normal tissue. The C_(t) values of both the calibrator and the samples of interest are normalized to an appropriate endogenous housekeeping gene.

Example 8 High Throughput Screening

High throughput screening (HTS) methods are used to identify compounds that inhibit candidate genes which are related to drug resistance and reduced survival rate. HTS methods involve providing a combinatorial chemical or peptide library containing a large number of potential therapeutic compounds. Such “libraries” are then screened in one or more assays, as described herein, to identify those library members (particular peptides, chemical species or subclasses) that display the desired characteristic activity. The compounds thus identified can serve as conventional “lead compounds” or can themselves be used as potential or actual therapeutics.

A combinatorial chemical library is a collection of diverse chemical compounds generated by either chemical synthesis or biological synthesis, by combining a number of chemical “building blocks” such as reagents. For example, a linear combinatorial chemical library such as a polypeptide library is formed by combining a set of chemical building blocks (amino acids) in every possible way for a given compound length (i.e., the number of amino acids in a polypeptide compound). Millions of chemical compounds can be synthesized through such combinatorial mixing of chemical building blocks.

Preparation and screening of combinatorial chemical libraries is well known to those of skill in the art. Such combinatorial chemical libraries include, but are not limited to, peptide libraries (see, e.g., U.S. Pat. No. 5,010,175, Furka, Int. J. Pept. Prot. Res. 37:487-493 (1991) and Houghton et al., Nature 354:84-88 (1991)). Other chemistries for generating chemical diversity libraries can also be used. Such chemistries include, but are not limited to: peptoids (e.g., PCT Publication No. WO 91/19735), encoded peptides (e.g., PCT Publication WO 93/20242), random bio-oligomers (e.g., PCT Publication No. WO 92/00091), benzodiazepines (e.g., U.S. Pat. No. 5,288,514), diversomers such as hydantoins, benzodiazepines and dipeptides (Hobbs et al., Proc. Nat. Acad. Sci. USA 90:6909-6913 (1993)), vinylogous polypeptides (Hagihara et al., J. Amer. Chem. Soc. 114:6568 (1992)), nonpeptidal peptidomimetics with glucose scaffolding (Hirschmann et al., J. Amer. Chem. Soc. 114:9217-9218 (1992)), analogous organic syntheses of small compound libraries (Chen et al., J. Amer. Chem. Soc. 116:2661 (1994)), oligocarbamates (Cho et al., Science 261:1303 (1993)), and/or peptidyl phosphonates (Campbell et al., J. Org. Chem. 59:658 (1994)), nucleic acid libraries (see Ausubel, Berger and Sambrook, all supra), peptide nucleic acid libraries (see, e.g., U.S. Pat. No. 5,539,083), antibody libraries (see, e.g., Vaughn et al, Nature Biotechnology, 14(3):309-314 (1996) and PCT/US96/10287), carbohydrate libraries (see, e.g., Liang et al., Science, 274:1520-1522 (1996) and U.S. Pat. No. 5,593,853), small organic molecule libraries (see, e.g., benzodiazepines, Baum C&EN, January 18, page 33 (1993); isoprenoids, U.S. Pat. No. 5,569,588; thiazolidinones and metathiazanones, U.S. Pat. No. 5,549,974; pyrrolidines, U.S. Pat. Nos. 5,525,735 and 5,519,134; morpholino compounds, U.S. Pat. Nos. 5,506,337; benzodiazepines, 5,288,514, and the like).

Devices for the preparation of combinatorial libraries are commercially available (see, e.g., ECIS™, Applied BioPhysics Inc., Troy, N.Y., MPS, 390 MPS, Advanced Chem Tech, Louisville Ky., Symphony, Rainin, Woburn, Mass., 433A Applied Biosystems, Foster City, Calif., 9050 Plus, Millipore, Bedford, Mass.). In addition, numerous combinatorial libraries are themselves commercially available (see, e.g., ComGenex, Princeton, N.J., Tripos, Inc., St. Louis, Mo., 3D Pharmaceuticals, Exton, Pa., Martek Biosciences, Columbia, Md., etc.).

Example 9 Inhibitor Oligonucleotide and RNA Interference (RNAi) Sequence Design

Known methods are used to identify sequences that inhibit candidate genes which are related to drug resistance and reduced survival rate. Such inhibitors may include but are not limited to, siRNA oligonucleotides, antisense oligonucleotides, peptide inhibitors and aptamer sequences that bind and act to inhibit PVT1 expression and/or function.

RNA interference is used to generate small double-stranded RNA (small interference RNA or siRNA) inhibitors to affect the expression of a candidate gene generally through cleaving and destroying its cognate RNA. Small interference RNA (siRNA) is typically 19-22 nt double-stranded RNA. siRNA can be obtained by chemical synthesis or by DNA-vector based RNAi technology. Using DNA vector based siRNA technology, a small DNA insert (about 70 bp) encoding a short hairpin RNA targeting the gene of interest is cloned into a commercially available vector. The insert-containing vector can be transfected into the cell, and expressing the short hairpin RNA. The hairpin RNA is rapidly processed by the cellular machinery into 19-22 nt double stranded RNA (siRNA). In a preferred embodiment, the siRNA is inserted into a suitable RNAi vector because siRNA made synthetically tends to be less stable and not as effective in transfection.

siRNA can be made using methods and algorithms such as those described by Wang L, Mu FY. (2004) A Web-based Design Center for Vector-based siRNA and siRNA cassette. Bioinformatics. (In press); Khvorova A, Reynolds A, Jayasena S D. (2003) Functional siRNAs and miRNAs exhibit strand bias. Cell. 115(2):209-16; Harborth J, Elbashir S M, Vandenburgh K, Manning a H, Scaringe S A, Weber K, Tuschl T. (2003) Sequence, chemical, and structural variation of small interfering RNAs and short hairpin RNAs and the effect on mammalian gene silencing. Antisense Nucleic Acid Drug Dev. 13(2):83-105; Reynolds A, Leake D, Boese Q, Scaringe S, Marshall WS, Khvorova A. (2004) Rational siRNA design for RNA interference. Nat Biotechnol. 22(3):326-30 and Ui-Tei K, Naito Y, Takahashi F, Haraguchi T, Ohki-Hamazaki H, Juni A, Ueda R, Saigo K. (2004) Guidelines for the selection of highly effective siRNA sequences for mammalian and chick RNA interference. Nucleic Acids Res. 32(3):936-48, which are hereby incorporated by reference.

Other tools for constructing siRNA sequences are web tools such as the siRNA Target Finder and Construct Builder available from GenScript (http://www.genscript.com), Oligo Design and Analysis Tools from Integrated DNA Technologies (URL:<http://www.idtdna.com/SciTools/SciTools.aspx>), or siDESIGN™ Center from Dharmacon, Inc. (URL:<http://design.dharmacon.com/default.aspx?source=0>). siRNA are suggested to built using the ORF (open reading frame) as the target selecting region, preferably 50-100 nt downstream of the start codon. Because siRNAs function at the mRNA level, not at the protein level, to design an siRNA, the precise target mRNA nucleotide sequence may be required. Due to the degenerate nature of the genetic code and codon bias, it is difficult to accurately predict the correct nucleotide sequence from the peptide sequence. Additionally, since the function of siRNAs is to cleave mRNA sequences, it is important to use the mRNA nucleotide sequence and not the genomic sequence for siRNA design, although as noted in the Examples, the genomic sequence can be successfully used for siRNA design. However, designs using genomic information might inadvertently target introns and as a result the siRNA would not be functional for silencing the corresponding mRNA.

Rational siRNA design should also minimize off-target effects which often arise from partial complementarity of the sense or antisense strands to an unintended target. These effects are known to have a concentration dependence and one way to minimize off-target effects is often by reducing siRNA concentrations. Another way to minimize such off-target effects is to screen the siRNA for target specificity.

The siRNA can be modified on the 5′-end of the sense strand to present compounds such as fluorescent dyes, chemical groups, or polar groups. Modification at the 5′-end of the antisense strand has been shown to interfere with siRNA silencing activity and therefore this position is not recommended for modification. Modifications at the other three termini have been shown to have minimal to no effect on silencing activity.

It is recommended that primers be designed to bracket one of the siRNA cleavage sites as this will help eliminate possible bias in the data (i.e., one of the primers should be upstream of the cleavage site, the other should be downstream of the cleavage site). Bias may be introduced into the experiment if the PCR amplifies either 5′ or 3′ of a cleavage site, in part because it is difficult to anticipate how long the cleaved mRNA product may persist prior to being degraded. If the amplified region contains the cleavage site, then no amplification can occur if the siRNA has performed its function.

Antisense oligonucleotides (“oligos”) can be designed to inhibit candidate gene function. Antisense oligonucleotides are short single-stranded nucleic acids, which function by selectively hybridizing to their target mRNA, thereby blocking translation. Translation is inhibited by either RNase H nuclease activity at the DNA:RNA duplex, or by inhibiting ribosome progression, thereby inhibiting protein synthesis. This results in discontinued synthesis and subsequent loss of function of the protein for which the target mRNA encodes.

In a preferred embodiment, antisense oligos are phosphorothioated upon synthesis and purification, and are usually 18-22 bases in length. It is contemplated that the candidate gene antisense oligos may have other modifications such as 2′-O-Methyl RNA, methylphosphonates, chimeric oligos, modified bases and many others modifications, including fluorescent oligos.

In a preferred embodiment, active antisense oligos should be compared against control oligos that have the same general chemistry, base composition, and length as the antisense oligo. These can include inverse sequences, scrambled sequences, and sense sequences. The inverse and scrambled are recommended because they have the same base composition, thus same molecular weight and Tm as the active antisense oligonucleotides. Rational antisense oligo design should consider, for example, that the antisense oligos do not anneal to an unintended mRNA or do not contain motifs known to invoke immunostimulatory responses such as four contiguous G residues, palindromes of 6 or more bases and CG motifs.

Antisense oligonucleotides can be used in vitro in most cell types with good results. However, some cell types require the use of transfection reagents to effect efficient transport into cellular interiors. It is recommended that optimization experiments be performed by using differing final oligonucleotide concentrations in the 1-5 μm range with in most cases the addition of transfection reagents. The window of opportunity, i.e., that concentration where you will obtain a reproducible antisense effect, may be quite narrow, where above that range you may experience confusing non-specific, non-antisense effects, and below that range you may not see any results at all. In a preferred embodiment, down regulation of the targeted mRNA will be demonstrated by use of techniques such as northern blot, real-time PCR, cDNA/oligo array or western blot. The same endpoints can be made for in vivo experiments, while also assessing behavioral endpoints.

For cell culture, antisense oligonucleotides should be re-suspended in sterile nuclease-free water (the use of DEPC-treated water is not recommended). Antisense oligonucleotides can be purified, lyophilized, and ready for use upon re-suspension. Upon suspension, antisense oligonucleotide stock solutions may be frozen at −20° C. and stable for several weeks.

Aptamer sequences which bind to specific RNA or DNA sequences can be made. Aptamer sequences can be isolated through methods such as those disclosed in co-pending U.S. patent application Ser. No. 10/934,856, entitled, “Aptamers and Methods for their Invitro Selection and Uses Thereof,” which is hereby incorporated by reference.

It is contemplated that the sequences described herein may be varied to result in substantially homologous sequences which retain the same function as the original. As used herein, a polynucleotide or fragment thereof is “substantially homologous” (or “substantially similar”) to another if, when optimally aligned (with appropriate nucleotide insertions or deletions) with the other polynucleotide (or its complementary strand), using an alignment program such as BLASTN (Altschul, S. F., Gish, W., Miller, W., Myers, E.W. & Lipman, D. J. (1990) “Basic local alignment search tool.” J. Mol. Biol. 215:403-410), and there is nucleotide sequence identity in at least about 80%, preferably at least about 90%, and more preferably at least about 95-98% of the nucleotide bases.

Example 10 Inhibition of ADAM9 Induces Cell Apoptosis

It was found that silencing of ADAM9 inhibits breast cancer cell growth and cell proliferation and inhibition of ADAM9 expression in breast cancer cells induces cell apoptosis. Thus, ADAM9 is implicated in proliferative aspects of breast cancer pathophysiology and serves as a possible therapeutic target in breast cancer.

A comprehensive study of gene expression and copy number in primary breast cancers and breast cancer cell lines was carried out, whereby we identified a region of high level amplification on chromosome 8p11 that is associated with reduced survival duration. The metalloproteinase-like, disintegrin-link and cysteine-rich protein, ADAM9, identified herein, maps to the region of amplification at 8p11. siRNA knockdown was applied to explore how amplification and over-expression of this particular gene play a role in breast cancer pathophysiology and to determine if this gene may be a valuable therapeutic target.

We transiently transfected 83 nM of siRNA for ADAM9 into T47D, BT549, SUM52PE, 600 MPE and MCF10A breast cancer cell lines. Non-specific siRNA served as a negative control. Cell viability/proliferation was evaluated by CellTiter-Glo® luminescent cell viability assay (CTG, Promega), cell apoptosis was assayed using YoPro-1 and Hoechst staining and cell cycle inhibition was assessed by measuring BrdU incorporation. All cellular measurements were made in adhered cells using the Cellomics high content scanning instrument. All assays were run at 3, 4, 5 and 6 days post transfection.

Briefly, the siRNA transfection protocol was as follows. Cells are plated and grown to 50-70% confluency and transfected using DharmaFECT1. In tubes, mix: Tube A: total volume 10 ul 9.5 uL SFM media+0.5siRNA (varied according to the experiment design); Tube B: total volume 10 ul 9.8 uL SFM media+0.2 DharmaFECT1. Incubate tubes for 5 min. During this incubation, remove media from target cells and replace with SFM in each well. Add contents of Tube B to Tube A and mix gently. Incubate for 20 min at room temperature. Add 20 uL mixture solution dropwise to each well (final volume=100 uL). Leave for 4 h, aspirate off media and replace with full growth media and allow cells to grow for several days.

Cell growth analysis was carried out using the CellTitter-Glo® Luminescent Cell Ciability Assay (Promega Cat#G7571/2/3). The luminescence signal of viable cells as measured the amount of ATP detected in the plates were read using a custom plate reader and program.

BrdU Staining and Fixation for Cellomics were used to measure cell proliferation and cell cycle analysis. To incorporate BrdU and fix the cells 10 uM final concentration of BrdU (Sigma #B5002) was added directly to cell media and pulsed for 30 minutes in tissue culture incubator. The media was removed and the cells washed 2× with 1×PBS and then 70% EtOH added to cover cells and fix for overnight at 4° C. Next day the 70% EtOH was removed and cells allowed to dry. Then 2N HCl was added and cells incubated at room temperature for 5-10 minutes, then removed and 1×PBS added to neutralize. Diluted anti-BrdU antibody (Mouse anti-BrdU Clone 3D4 (BD Pharmingen #555627)) 1:100 in 1×PBS/0.5% Tween-20. Anti-BrdU was added to cells (50 ul-96 well plate; 200 ul-24 well plate) and incubated for 45-60 minutes at room temperature on a rocker. Antibody was aspirated and cells washed 2× with 1×PBS/0.5% Tween-20. Rabbit Anti-mouse Alexa Fluor 488 (Invitrogen #A-11059) was diluted 1:250 in 1×PBS/0.5% Tween-20. Secondary antibody was added to cells and incubated 30-60 minutes at room temperature on a rocker then washed 3× with 1×PBS/0.5% Tween-20. After the last wash was removed and cells were incubated with 1 ug/ml Hoechst 33342 (Sigma #B2261) diluted in 1×PBS for 45 minutes at room temperature on a rocker. Cells were washed and covered with 1×PBS. Plates were scanned or stored at 4° C. for later scanning on Cellomics.

YoPro-1 Staining for Cellomics was used for cell apoptosis analysis. Add YoPro-1(Final use at 1 ug/ml) and Hoechst (Final use at 10 ug/ml) directly to cell media. Place in 37° C. incubator for 30 min. Then read directly on Cellomics Significant knockdown of ADAM9 was achieved in BT549 and T47D cells transfected with siRNA-ADAM9 for 48 hr, 72 hr and 96 hr. Silencing of ADAM9 significantly reduced the proliferation of breast cancer cells and inhibited the BrdU incorporation after treatment with siRNA compared to controls. Knockdown of ADAM9 in breast cancer cells also induced significant levels of apoptosis. Furthermore, we found that cells had very good response when the concentration of siRNA-ADAM9 were higher than 30 nM. The current results suggested that silencing expression of ADAM9 is a novel approach for inhibition of breast cancer cell growth. ADAM9 may serve as a new candidate therapeutic target for treatment of breast cancer with poor outcome.

Example 11 Inhibition of Genes Encoded by the 11q13 Amplicon

As described above, the 11q13 amplicon encodes ten genes or non-coding RNA transcripts that appear likely to contribute to the pathophysiology of breast cancer and that are potential therapeutic targets. None of these genes are considered druggable based on predicted protein folding characteristics. However, all are candidates for siRNA therapeutic attack. We applied an efficient siRNA transfection strategy as explained in Example 9 to assess the therapeutic potential of siRNAs against genes encoded in the region of recurrent amplification at 11q13.

We transiently transfect 50 nM of siRNAs targeting these genes (4 individual siRNAs per gene, Table 8) in cell lines amplified at 11q13 (HCC1954, ZR75B, MDAMB415 and CAMA1) and not amplified (BT474, HS578T and MCF10A). Non-specific siRNA served as a negative control Viable cell number and apoptosis index were measured for each siRNA. These analyses showed that silencing of CCND1, FGF3, PPFIA1, FOLR3, and NEU3 reduced the cell growth of 11q13-amplified breast cancer cells compared to unamplified controls (FIG. 11). Knockdown of FGF3, PPFIA1 and NEU3 also induced cell apoptosis in amplified cell lines (HCC1954 and ZR75B), but not in non-amplified lines MCF10A (FIG. 12).

To further validated the therapeutic potential of targeting FGF3, PPFIA1 and NEU3, we packaged shRNA lenti-virus (5 shRNAs for each gene, Open Biosystems Inc. Table 9 using the third generation lenti-virus packaging system and infected breast cells in which amplified/overexpressed FGF3, PPFIA1 and NEU3 are overexpressed with these lentiviral shRNAs. Knockdown efficiency was then measured by western blot. We identified successful clones marked with arrows (at least one clone for each gene) that can knock down more than 80% protein of the target genes (FIG. 13).

Knockdown of FGF3, PPFIA1, and NEU3 also induced cell apoptosis and inhibited cell growth in 3D culture. We measured cell apoptosis by caspase3 activity and/or YoPRO plus Hoechst staining after cells infected with shRNAs using methodology described in Example 9. We found that knockdown of FGF3, PPFIA1 and NEU3 by shRNA significantly increased cell apoptosis in breast cancer cells (FIG. 14). We also examined the effects in a 3D culture system, which can mimic in vivo environments. Our data showed that when each of these three genes was silenced in CAMA1 and HCC1954 cells, the colonies were much smaller in comparison to the shRNA control that was also cultured in the 3D culture system (FIG. 15).

Combinational knockdown of genes at 11q13 amplicon has the synergistic effect in breast cancer cells.

To evaluate the synergistic effect on knockdown of candidate therapeutic targets FGF3, PPFIA1, NEU3 and CCND1, we infected breast cancer cells with shRNAs lentivirus individually and/or combinationally. Our data showed that combinational knockdown of NEU3 and PPFIA1 significantly inhibited cell growth (FIG. 16A). Combinational knockdown of NEU3 and PPFIA1 also increased cell apoptosis dramatically (FIG. 16B), which is consistent with our cell growth data. Our findings indicated that knockdown of NEU3 and PPFIA1 at the same time has a singificant synergistic effect on cell growth inhibition and cell apoptosis. In summary, the data in these examples show that FGF3, PPFIA1, and NEU3 and the combination of NEU3 and PPFIA1, in particular, are potential therapeutic targets in breast cancer cells.

Example 12 Inhibition of Genes Encoded by the 20q13 Amplicon

As described above, the 20q13 amplicon encodes fourteen genes or non-coding RNA transcripts that appear likely to contribute to the pathophysiology of breast cancer and that are potential therapeutic targets. None of these genes are considered druggable based on predicted protein folding characteristics. However, all are candidates for siRNA therapeutic attack. We applied an efficient siRNA transfection strategy as explained in Example 9 to assess the therapeutic potential of siRNAs against genes encoded in the region of recurrent amplification at 20q13. We transiently transfected 50 nM of siRNAs (Table 10) targeting these genes (4 individual siRNAs per gene) in cell lines amplified at 20q13 (BT474, MCF7, MDAMB157 and SUM52PE) and not amplified (MCF10A and ZR75B). Non-specific siRNA served as a negative control. Viable cell number, proliferation and apoptosis index were measured for each siRNA using the assays described in Example 9. These analyses showed that silencing of CSTF1, PCK1, RAB22A, VAPB, GNAS, C20orf45, BCAS1, TMEPA1 and STX16 reduced the cell growth of 20q13-amplified breast cancer cells compared to unamplified controls (FIG. 17). Knockdown of VAPB, GNAS, TMEPAI and STX16 also inhibited cell proliferation (the percentage of S-phase cells) and induced cell apoptosis in amplified cell lines MCF7 and SUM52PE cells (FIGS. 18 and 19). Caspase 3 activity also increased in SUM52PE cells treated for 72 hours with VAPB, GNAS, TMEPAI and STX16 siRNAs (FIG. 20). siGNAS also knocked down Gs transcripts in the amplified cell lines (FIG. 21). These results indicate that therapeutic strategies, e.g., targeting CSTF1, PCK1, VAPB, GNAS, BCAS1, TMEPAI and STX16 genes, may be particularly effective in treating patients with 20q13 amplification.

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While the present sequences, compositions and processes have been described with reference to specific details of certain exemplary embodiments thereof, it is not intended that such details be regarded as limitations upon the scope of the invention. The present examples, methods, procedures, specific compounds and molecules are meant to exemplify and illustrate the invention and should in no way be seen as limiting the scope of the invention. Any patents, publications, publicly available sequences mentioned in this specification and listed above are indicative of levels of those skilled in the art to which the invention pertains and are hereby incorporated by reference to the same extent as if each were specifically and individually incorporated by reference.

TABLE 1 Univariate and multivariate associations for individual amplicons and/or disease specific survival and distant recurrence. Also shown are the chromosomal positions of the beginning and ends of the amplicons and the flanking clones. Associations are shown for the entire sample set and for luminal A tumors (univariate associations only). p-value Flanking p-value luminal A, p-value Flanking clone univariate univariate multivariate Amplicon clone (left) (right) kbStart kbEnd survival recurrence survival recurrence survival recurrence 8p11-12 RP11- RP11- 33579 43001 0.011 0.004 0.022 0.004 0.037 0.006 258M15 73M19 8q24 RP11-65D17 RP11- 127186 132829 0.830 0.880 0.140 1.0 0.870 0.720 94M13 11q13-14 CTD-2080I19 RP11- 68482 71659 0.540 0.410 0.016 0.240 0.660 0.440 256P19 11q13-14 RP11- RP11- 73337 78686 0.230 0.150 0.016 0.240 0.360 0.190 102M18 215H8 12q13-14 BAL12B2624 RP11- 67191 74053 0.250 0.260 0.230 0.098 0.920 0.960 92P22 17q11-12 RP11-58O8 RP11- 34027 38681 0.004 0.004 1.0 1.0 0.022/ 0.008 87N6 17q21-24 RP11-234J24 RP11- 45775 70598 0.960 0.920 0.610 0.290 0.530 0.630 84E24 20q13 RMC20B4135 RP11- 51669 53455 0.340 0.800 0.048 0.140 0.590 0.970 278I13 20q13 GS-32I19 RP11- 55630 59444 0.087 0.230 0.048 0.140 0.060 0.220 94A18 Any 0.005 0.003 0.024 0.120 0.034 0.009 ammplicon

TABLE 2 Associations of genomic variables with clinical features. Number of Presence of Fraction of Total number of Number of recurrent recurrent genome altered¹ transitions² amplified arms³ amplicons⁴ amplicons⁵ 1. ER (neg vs. pos) <0.001 <0.001 0.376 0.147 0.482 2. PR (neg vs. pos) 0.005 <0.001 <0.050 0.319 0.390 3. Nodes (pos vs. neg) 0.053 0.106 0.012 0.012 0.008 4. Stage (>1 vs. 1) 0.013 0.052 0.045 0.312 0.368 5. ERBB2 (pos vs. neg) 0.650 0.830 0.015 <.001 <0.001 6. Ki67 (>0.1 vs. <0.1) 0.013 0.031 0.024 0.010 0.005 7. P53 (pos vs. neg) 0.001 <0.001 0.043 0.573 0.171 8. Size 0.339 0.088 0.016 0.005 0.015 9. Age at Dx 0.767 0.361 0.223 0.905 0.947 10. SBR Grade <0.001 <0.001 0.008 0.206 0.035 11. Expression subtype <0.001 <0.001 0.002 0.003 <0.001 12. Genomic subtype <0.001 <0.001 <0.001 <0.001 <0.001 ^(1,2,3,4)Kruskal-wallis test (1-7, 11, 12), significance of robust linear regression standardized coefficient (8-10 ⁵Fisher exact test (1-7, 11, 12), significance of robust linear regression standardized coefficient (8-10)

TABLE 3 Functional characteristics of genes in recurrent amplicons associated with reduced survival duration in breast cancer. Functional annotation was based on the Human Protein Reference Database at the http address hprd.org. Genes highlighted in dark gray are associated with reduced survival duration or distant recurrence when over expressed in non-Amplifying tumors. Genes highlighted in light gray are significantly associated with reduced survival duration or distant recurrence (p < 0.05) when down regulated in non-Amplifying tumors. Distances to sites of recurrent viral integration were determined from published information (Akagi et al., 2004). The last column identifies genes having predicted protein folding characteristics suggesting that they might bedruggable (Russ and Lampel, 2005).

TABLE 4 Univariate p-values with the corresponding 95% confidence intervals for associations with disease-specific survival and distant recurrence emdpoints and the corresponding multivariate results for those found to be significant in univariate analyses (p < .05) for at least one of the clinical end points. Only variables individually significant at p < .05 for at least one of the two end points are included in the multivariate regression. Stage and SBR Grade are treated as continuous variables rather then factors. In each column pair, the left subcolumn lists results for disease-specific survival and the right subcolumn lists results for time to distant recurrence. Hazard Hazard p-value ratio Confidence interval p-value ratio Confidence interval univariate univariate univariate multivariate multivariate multivariate Size <1e−03 <1e−03 1.6 1.6 1.3, 2   1.3, 2   0.012 0.005 1.5 1.7 1.1, 2.1 1.2, 2.4 Nodal status 0.001 0.016 3.8 2.5 1.7, 8.5 1.2, 5.4 0.034 0.1 3.0 2.4 1.1, 8.7 0.9, 6.7 Stage <1e−03 0.007 2.9 2.3 1.7, 5.2 1.3, 4.1 0.690 0.32 0.6 0.3, 2.3 0.2, 1.8 ER 0.29 0.74 0.7 0.9 0.3, 1.4 0.4, 1.8 PR 0.14 0.13 0.6 0.6 0.3, 1.2 0.3, 1.2 ERBB2 0.2 0.11 1.8 2.1 0.7, 4.4 0.8, 5.3 P53 0.82 0.07 1.1 2.1 0.5, 2.5   1, 4.5 Ki67 0.64 0.41 1.2 1.4 0.5, 2.7 0.6, 3.4 SBR Grade 0.095 0.11 1.6 1.6 0.9, 2.8 0.9, 2.9

TABLE 5 Comparison of the association between expression subtypes and survival duration in 3 datastes. Leg-likelihood ratio test p-value is shown for each model. Basal is the reference in all models. Multivariate models include size and nodal status. In multivariate analyses, the first value shown in each cell is the p-value and the second is the ratio of the medians in the compared groups Hazard Confidence ratio interval p-value Hazard ratio Confidence interval p-value univariate univariate multivariate multivariate multivariate multivariate This study 0.004 0.024 2e−05 1e−04 Basal reference 1 1 1 1 ERBB2 0.02 0.008 3.4 5.1  1.2, 9.3 1.5, 116.8 0.49 0.07 1.5 3.2  0.5, 4.7 0.9, 11.6 Luminal A 0.19 0.45 0.5 0.6  0.2, 1.4 0.2, 2.1  0.1 0.32 0.4 0.5  0.1, 1.2 0.2, 1.9  Luminal B 0.18 0.88 0.24 1.1 0.03, 1.9 0.3, 4.7  0.1 0.87 0.2 0.9 0.02, 1.4 0.2, 3.9  Normal-like 0.19 0.70 0.25 0.7 0.03, 2   0.1, 3.7  0.14 0.63 0.2 0.7 0.03, 1.7 0.1, 3.5  van de Vijver et 0.0006 0.18 al¹ Basal reference 1 1 ERBB2 0.14 0.95 0.6 1  0.3, 1.2 0.5, 1.8  Luminal A 3.5e−05 0.1 0.3 0.6  0.2, 0.5 0.4, 1.1  Luminal B 0.23 0.7 0.6 1.1  0.3, 1.3 0.6, 2.3  Normal-like 0.01 0.23 0.3 0.7  0.2, 0.8 0.3, 1.4  Sorlie et al² 2e−06 Basal ref 1 ERBB2 0.83 1.11  0.4, 2.9 Luminal A 0.001 .04 0.005, .3  Luminal B 0.27 0.6  0.2, 1.6 Normal-like 1 0 0, Inf ¹van de Vijver MJ, He YD, van't Veer LJ, Dai H. Hart AA, et al. 2002) A gene-expression signature as a predictor of survival in breast cancer. N Engl J Med 347. 1999-2009. ²Sorlie T, Perou CM, Tibshirani R, Aas T, Geisler S, et al. 2001) Gene expression patterns of breast carcinomas distinguishing tumor subclasses with clinical implications. Proc Natl Acad Sci U S A 98: 10869-10874.

TABLE 6 Identities of 1432 gene transcripts showing significant associations between genome copy numbers measured using array CGH and transcript levels measured using Affymetrix U133A expression arrays in 101 primary breast tumors. Data will be available through CaBIG and a public web site. Kbp Kbp Gene Cor Chr Chrom Genome transcript Pearson 1 6295 6295 FLJ23323 0.54 1 7731 7731 PARK7 0.48 1 10231 10231 DFFA 0.46 1 10676 10676 FRAP1 0.53 1 11750 11750 MFN2 0.51 1 12047 12047 VPS13D 0.45 1 13394 13394 PRDM2 0.56 1 15216 15216 KIAA0962 0.48 1 15537 15537 SPEN 0.48 1 15958 15958 FBXO42 0.53 1 26364 26364 DHDDS 0.47 1 27339 27339 WASF2 0.49 1 32619 32619 RBBP4 0.52 1 32744 32744 YARS 0.46 1 36349 36349 MRPS15 0.46 1 37460 37460 GNL2 0.54 1 37892 37892 CGI-94 0.53 1 38718 38718 RRAGC 0.51 1 39210 39210 BACF1 0.48 1 39440 39440 PABPC4 0.59 1 39618 39618 PPIE 0.59 1 39720 39720 TRIT1 0.58 1 40040 40040 RLF 0.64 1 40329 40329 ZNF643 0.67 1 40500 40500 RIMS3 0.65 1 40571 40571 NFYC 0.59 1 40859 40859 CTPS 0.51 1 40906 40906 SCMH1 0.60 1 42561 42561 LOC442610 0.54 1 42561 42561 NSEP1 0.58 1 42646 42646 C1orf50 0.61 1 43043 43043 EBNA1BP2 0.47 1 43242 43242 ELOVL1 0.58 1 43263 43263 MED8 0.56 1 43322 43322 KIAA0467 0.47 1 43410 43410 PTPRF 0.51 1 43529 43529 LMLD2A 0.62 1 43849 43849 DPH2L2 0.53 1 43858 43858 B4GALT2 0.45 1 44100 44100 PRNPIP 0.61 1 44511 44511 FLJ0597 0.45 1 44730 44730 EIF2B3 0.63 1 44891 44891 UROD 0.45 1 45208 45208 MUTYH 0.46 1 45463 45463 SP192 0.57 1 53063 53063 MAGOH 0.45 1 54063 54063 SSBP3 0.45 1 54551 54551 TTC4 0.58 1 54902 54902 USP24 0.56 1 61578 61578 INADL 0.51 1 67248 67248 PAI-RBP1 0.47 1 77453 77453 ZZZ3 0.54 1 84532 84532 SSX2IP 0.47 1 87217 87217 LMO4 0.52 1 88617 88617 PKN2 0.47 1 88786 88786 GTF2B 0.49 1 89754 89754 LRRC5 0.50 1 92184 92184 GLMN 0.51 1 92769 92769 RPL5 0.63 1 93017 93017 M96 0.49 1 93807 93807 ERBP 0.48 1 100918 100918 CGI-30 0.49 1 109055 109055 SARS 0.44 1 113747 113747 DCLRE1B 0.49 1 114239 114239 TRIM33 0.64 1 114409 114409 BCAS2 0.61 1 114558 114558 UNR 0.50 1 114614 114614 FLJ21168 0.74 1 117414 117414 MAN1A2 0.49 1 143265 143265 PEX11B 0.57 1 143341 143341 POLR3C 0.51 1 144520 144520 BCL9 0.51 1 147088 147088 CGI-143 0.46 1 147112 147112 SF3B4 0.63 1 147256 147256 VPS45A 0.47 1 147454 147454 APH1A 0.52 1 147661 147661 KIAA0460 0.52 1 147677 147677 TARSL1 0.57 1 147677 147677 TARSL1 0.57 1 147813 147813 ENSA 0.45 1 147835 147835 GOLPH3L 0.51 1 148116 148116 SETDB1 0.49 1 148355 148355 SCNM1 0.48 1 148366 148366 TCFL1 0.59 1 148481 148481 PIK4CB 0.53 1 148592 148592 POGZ 0.56 1 148801 148801 SNX27 0.57 1 148949 148949 MRPL9 0.51 1 153241 153241 MAPBPIP 0.55 1 153383 153383 KIAA0446 0.47 1 153400 153400 PMF1 0.46 1 153909 153909 FLJ12671 0.52 1 153924 153294 MRPL24 0.55 1 153954 153954 PRCC 0.54 1 154122 154122 ARGHEF11 0.44 1 157392 157392 PEA15 0.49 1 157463 157463 PEX19 0.54 1 157476 157476 COPA 0.49 1 157530 157530 NCSTN 0.55 1 158287 158287 PFDN2 0.47 1 158305 158305 NIT1 0.49 1 158306 158306 DEDD 0.50 1 158340 158340 Ufc1 0.57 1 158346 158346 USP21 0.56 1 158353 158353 PPOX 0.46 1 158358 158358 B4GALT3 0.65 1 158386 158386 NDUFS2 0.64 1 158501 158501 SDHC 0.56 1 158907 158907 DUSP12 0.53 1 158923 158923 ATF6 0.52 1 159719 159719 UAP1 0.54 1 162819 162819 ALDH9A1 0.45 1 162884 162884 LOC54499 0.53 1 163996 163996 POGK 0.54 1 166525 166525 BLZF1 0.44 1 166949 166949 MGC9064 0.45 1 167010 167010 SCYL3 0.53 1 168721 168721 BAT2D1 0.47 1 168901 168909 VAMP4 0.51 1 168990 168990 KIAA0859 0.66 1 169650 169650 PIGC 0.61 1 170923 170923 KLHL20 0.68 1 172209 172209 CACYBP 0.48 1 172223 172223 MRPS14 0.57 1 172365 172365 KIAA0040 0.47 1 177071 177071 LOC163590 0.46 1 177091 177091 LAP18 0.52 1 178182 178182 STX6 0.56 1 181899 181899 C1orf22 0.45 1 183522 183522 TPR 0.57 1 183584 183854 C1orf27 0.447 1 190317 190317 SSA2 0.49 1 197664 197664 ZNF281 0.50 1 198107 198107 KIAA1078 0.48 1 199087 199087 IPO9 0.53 1 199240 199240 RMPEP 0.53 1 199985 199985 JARD1B 0.55 1 200136 200136 RABIF 0.65 1 200198 200198 ADIPOR1 0.58 1 200281 200281 C1ord37 0.51 1 201008 201008 SNRPE 0.50 1 203849 203849 LGTN 0.61 1 205009 205009 MCP 0.57 1 207038 207038 IRF56 0.51 1 207081 207081 MGC29875 0.58 1 208513 208513 RCOR3 0.48 1 208998 208998 LPGAT1 0.46 1 209663 209663 SCIRP10 0.47 1 210225 210225 LOC90806 0.55 1 210281 210281 RPS6KC1 0.45 1 212797 212797 KCTD3 0.58 1 215515 215515 CGI-115 0.46 1 217330 217330 FLJ10326- 0.55 1 217380 217380 RAB3- 0.59 GAP150 1 217978 217978 FLJ20605 0.50 1 221276 221276 FBXO28 0.54 1 221346 221346 DEGS1 0.52 1 221390 221390 NVL 0.51 1 221519 221519 HSPC163 0.49 1 221550 221550 WDR26 0.45 1 223225 223225 H3F3A 0.54 1 223307 223307 ACBD3 0.61 1 225245 225245 ARF1 0.52 1 225263 225263 C1orf35 0.47 1 225303 225303 GUK1 0.49 1 226368 226368 RAB4A 0.53 1 226402 226402 APHAR 0.50 1 226538 226538 NUP133 0.48 1 228412 228412 GNPAT 0.55 1 228412 228412 GNPAT 0.55 1 232533 232533 GGPS1 0.55 1 232572 232572 TBCE 0.55 1 233755 233755 FLJ10359 0.49 1 241519 241519 ADSS 0.48 1 243651 243651 TFB2M 0.47 1 245947 245947 ZNF672 0.51 2 9651 255779 ADAM17 0.44 2 9651 255779 LOC285148 0.50 2 9746 255874 YWHAG 0.54 2 15329 261457 NAG 0.64 2 15754 261881 DDX1 0.68 2 16718 252846 FAM49A 0.51 2 28945 285073 SFRS7 0.45 2 64556 310784 HSPC159 0.49 2 85818 331946 USP39 0.45 2 96486 324614 BRRN1 0.47 2 172051 418179 TLK1 0.44 2 238888 485016 LRRFIP1 0.56 3 3167 492911 CRBN 0.52 3 4320 494064 SETMAR 0.45 3 5139 494883 ARL10C 0.52 3 10318 500062 SEC13L1 0.46 3 12574 502317 MKRN2 0.51 3 12600 502344 RAF1 0.49 3 13333 503077 NUP210 0.52 3 14162 503906 XPC 0.67 3 14964 504708 NR2C2 0.52 3 38041 527785 ACAA1 0.45 3 39054 528798 WDR48 0.49 3 39413 529157 RPSA 0.46 3 40459 530203 RPL14 0.50 3 44978 534722 EXOSC7 0.47 3 48748 538942 PRKAR2A 0.59 3 48919 538663 ARIH2 0.45 3 49026 538770 FLJ20259 0.44 3 49092 538836 QARS 0.52 3 50566 540310 HEMK1 0.45 3 51976 541720 ACY1 0.45 3 51986 541730 RPL29 0.45 3 52247 541991 PRO2730 0.47 3 52393 542137 BAP1 0.46 3 53279 543023 DCP1A 0.52 3 72347 562091 RYBP 0.50 3 72719 562463 SHQ1 0.62 3 109643 599386 DZIP3 0.59 3 114759 604503 MAK3 0.49 3 114787 604531 ATP6V1A 0.50 3 127477 617221 MGC11349 0.46 3 128613 618357 GPR175 0.49 3 129092 618836 SEC61A1 0.56 3 129660 619404 RPN1 0.53 3 129766 619510 RAB7 0.54 3 130319 620062 DC12 0.49 3 130471 620215 MBD4 0.55 3 130690 520434 TMCC1 0.49 3 135197 624941 RYK 0.48 3 135835 625579 EPHB1 0.54 3 137005 626749 PPP2R3A 0.58 3 137902 627646 NCK1 0.47 3 139534 629278 Cap70 0.45 3 140384 630128 MRPS22 0.55 3 142010 631754 FLJ10618 0.54 3 14401 633785 SR140 0.48 3 150030 639774 GYG 0.56 3 150557 640301 WWTR1 0.57 3 160312 650056 SCHIP1 0.44 3 181951 671695 FXR1 0.56 3 185194 674938 DVL3 0.49 3 187585 677329 FLJ10560 0.46 3 197793 687537 PAK2 0.58 3 197988 687731 SENP5 0.59 3 197989 687733 NCBP2 0.57 3 198098 687842 DLG1 0.54 3 198725 688469 KIAA0226 0.47 4 1195 690283 CTBP1 0.48 4 1946 691034 WHSC2 0.49 4 2659 691747 C4ord8 0.59 4 2775 691863 TNIP2 0.49 4 2877 691965 ADD1 0.61 4 2964 692052 TETRAN 0.47 4 4486 692574 STX18 0.56 4 54159 743247 FIP1L1 0.60 4 56178 745266 TPARL 0.46 4 56214 745302 CLOCK 0.65 4 68488 757576 FLJ10808 0.55 4 69182 758270 YT521 0.53 4 69746 758834 TMPRSS11E 0.52 4 72020 761108 SAS10 0.61 4 72054 761142 LOC441022 0.59 4 72054 761142 RIPX 0.49 4 72152 761239 GRSF1 0.54 4 72326 761414 DCK 0.44 4 76865 765953 RCHY1 0.44 4 77026 766114 G3BP2 0.55 4 77108 766196 VDP 0.54 4 77329 766417 SDAD1 0.60 4 83733 772821 HNRPD 0.47 4 101279 790367 FLJ14281 0.44 4 104176 793264 UBE2D3 0.54 4 140556 8229644 ELF2 0.45 4 140789 829877 NDUFC1 0.46 4 140800 8229888 NARG1 0.48 4 142720 831808 ZNF330 0.45 4 185122 874210 ING2 0.45 5 271 881091 SDHA 0.45 5 496 881316 SEC6L1 0.46 5 946 881766 TRIP13 0.47 5 1514 882334 FLJ12443 0.48 5 1854 882674 NDUFS6 0.49 5 31447 912267 RNASE3L 0.69 5 31578 912398 FLJ11193 0.47 5 32273 913093 MTMR12 0.54 5 32273 913093 MTMR12 0.54 5 37022 917842 NIPBL 0.46 5 37152 917972 FLJ13231 0.50 5 37425 918245 FLJ10233 0.57 5 43169 923989 ZNF131 0.49 5 66426 947246 MAST4 0.46 5 77740 958560 SCAMP1 0.45 5 80800 961620 SSBP2 0.54 5 80800 961620 0.54 5 118864 999684 HSD17B4 0.45 5 131782 1012602 SLC22A5 0.52 5 133938 1014757 PHF15 0.46 5 134150 1014970 CAMLG 0.50 5 134746 1015566 H2AFY 0.48 5 139934 1020754 ANKHD1 0.49 5 14909 1030229 KIAA0194 0.48 5 179091 1059911 RUFY1 0.50 5 179270 1060089 MAML1 0.60 5 171399 1060219 KIAA0676 0.51 6 10488 1072343 PAK1P1 0.47 6 20510 1062365 E2F3 0.62 6 24532 1086387 MRS2L 0.51 6 24775 1086630 THEM2 0.48 6 24883 1086738 GMNN 0.54 6 25705 1088560 ABT1 0.45 6 31738 1093593 CSNK2B 0.51 6 32024 1093879 RDBP 0.51 6 36042 1097897 MAPK14 0.49 6 36509 1098363 STK38 0.49 6 41936 1103791 BYSL 0.45 6 43029 1104884 KLHDC3 0.54 6 43450 1105305 ABCC10 0.45 6 71373 1133228 SMAP1 0.55 6 75943 1137798 COXA7A2 0.48 6 76308 1138163 SENP6 0.54 6 83773 1145626 KIAA1117 0.50 6 87967 1149821 ZNF292 0.45 6 88042 1149897 C6orf62 0.447 6 89316 1151170 RNGTT 0.46 6 90349 1152204 MDN1 0.46 6 91190 1153045 MAP3K7 0.45 6 99894 1161749 C6orf111 0.48 6 105771 1167626 PREP 0.64 6 106678 1168533 APG5L 0.73 6 107123 1168978 QRSL1 0.61 6 107519 1169374 C6orf210 0.59 6 106260 1170115 SEC63 0.69 6 108522 1170377 SNX3 0.52 6 106927 1170782 FOXO3A 0.60 6 109829 1171684 ZBTB24 0.57 6 110547 1172402 CDC40 0.55 6 110977 1172832 CDC2L6 0.55 6 111242 1173096 AMD1 0.51 6 111666 1173521 REV3L 0.48 6 111864 1173719 RAF3IP2 0.61 6 114307 1176162 HDAC2 0.58 6 119180 1181035 C6orf61 0.66 6 119203 1181057 ASF1A 0.63 6 119262 1181116 C6orf60 0.55 6 125577 1187432 C6orf74 0.54 6 131876 1193731 CRSP3 0.59 6 132762 1194617 STX7 0.62 6 134255 1196110 TBPL1 0.53 6 135219 1197074 ALDH8A1 0.45 6 135266 1197121 HBS1L 0.59 6 138706 1200561 HEBP2 0.57 6 143753 1205608 PEX3 0.49 6 145927 1207782 EPM2A 0.52 6 160299 1222154 IGF2R 0.49 6 170701 1232556 PSMB1 0.47 6 170743 1232598 PDCD2 0.45 7 2019 1234788 FTSJ2 0.49 7 2139 1234908 EIF3S9 0.46 7 2188 1234957 CHST12 0.45 7 2343 1235113 IQCE 0.49 7 5808 1238577 EIF2AK1 0.50 7 6607 1239376 C7orf28B 0.44 7 7352 1240121 FLJ203223 0.54 7 7898 1240667 ICA1 0.51 7 27522 1260291 TAX1BP1 0.53 7 43412 1276181 FLJ10803 0.49 7 43656 1276426 URG4 0.54 7 44164 1276933 NUDCD3 0.59 7 44346 1277116 DDX56 0.56 7 55861 1288631 OCT6A 0.45 7 72129 1304899 NSUN5 0.53 7 72129 1204899 WBSCR20B 0.47 7 72267 1205036 BAZ1B 0.47 7 72363 1305132 BCL7B 0.47 7 72510 1305279 WBSCR20C 0.50 7 86567 1319336 TP53AP1 0.53 7 91689 1324458 GATAD1 0.53 7 95930 1328699 SHFM1 0.47 7 98088 1330857 TRRAP 0.50 7 98605 1331374 PSAP1 0.51 7 98618 1331387 G10 0.59 7 98628 1331398 PTCD1 0.60 7 98667 1331437 ATP5J2 0.56 7 98667 1331437 ATP5J2 0.56 7 98714 1331483 ZFP95 0.49 7 99822 1332591 MOSPD3 0.57 7 99829 1332599 TFR2 0.49 7 99915 1332685 POP7 0.47 7 100012 1332781 EPHB4 0.61 7 100062 1332831 SLC12A9 0.52 7 100422 1333191 ZNHIT1 0.62 7 101597 1334367 PRKRIP1 0.50 7 101674 1334444 POLR2J 0.47 7 102498 1335268 PMPCB 0.48 1 102513 1335283 ZRF1 0.65 7 103327 1336097 ORC5L 0.54 7 104733 1337503 RINT-1 0.52 7 128057 1360826 ATP6V1F 0.47 7 128151 1360921 TNPO3 0.46 7 138459 1371229 LUC7L2 0.46 7 139560 1372330 MKRN1 0.59 7 140113 1372883 MRPS33 0.49 7 140663 1373432 MULK 0.55 7 149460 1382229 REPIN1 0.46 7 150148 1382918 SLC4A2 0.51 7 150165 1382935 FASTK 0.47 7 150301 1383071 ABCF2 0.50 7 150556 1383325 RHEB 0.51 7 151115 1383884 GALNT11 0.47 7 158547 1389316 DNAJB6 0.45 8 1808 1393123 ARGHEF10 0.47 8 9676 1400991 TNKS 0.57 8 9949 1401264 MSRA 0.57 8 11698 1403013 FDFT1 0.50 8 17791 1409106 PCM1 0.60 8 21799 1413114 XPO7 0.48 8 21979 1413294 RAI16 0.50 8 21986 1413301 FLJ22494 0.49 8 22500 1413815 BIN3 0.60 8 22900 1414215 TNFRSF10B 0.52 8 23126 1414441 CHMP7 0.59 8 23168 1414482 LOC203069 0.64 8 23312 1314627 ENTPD4 0.44 8 26171 1417486 PPP2R2A 0.58 8 26262 1417577 BNIP3L 0.47 8 27371 1418685 EPHX2 0.58 8 27613 1418928 FLJ10853 0.63 8 48223 1439538 KIAA0146 0.50 8 48923 1440238 MCM4 0.48 8 48971 1440286 UBE2V2 0.49 8 53585 1444900 RB1CC1 0.46 8 54678 1445993 ATP6V1H 0.52 8 54929 1446244 TCEA1 0.46 8 55098 1446413 MRPL15 0.45 8 56736 1448051 NCOA6IP 0.55 8 57174 1448489 CHCHD7 0.47 8 64175 1455490 YTHDF3 0.49 8 66607 1457922 CHPPR 0.51 8 67391 1458706 RRS1 0.59 8 73971 1465286 TERF1 0.55 8 80881 1472196 MRPS28 0.49 8 81448 1472763 ZBTB10 0.45 8 82620 1473935 IMPA1 0.53 8 82664 1473979 ZFAND1 0.61 8 87443 1478758 CGI-90 0.46 8 90902 1482217 NBS1 0.50 8 95341 1486656 RAD54B 0.50 8 95818 1487133 FLJ20530 0.56 8 95849 1487164 CCNE2 0.48 8 97199 1488514 UQCR8 0.59 8 97208 1488523 CGI-12 0.60 8 97231 1488546 PTDSS1 0.60 8 98658 1489973 LYRIC 0.70 8 98744 1490059 LAPTM4B 0.48 8 99011 1490325 RPL30 0.57 8 99071 1490386 HRSP12 0.62 8 99097 1490412 POP1 0.63 8 99423 1490738 STK3 0.59 8 101119 1492434 POLR2K 0.66 8 101127 1492442 SPAG1 0.48 8 101226 1492541 RNF19 0.49 8 101490 1492805 LOC157567 0.57 8 101672 1492987 PABPC1 0.63 8 101887 1493202 YWHAZ 0.71 8 102136 1493451 LOC157562 0.75 8 102168 1493483 LOC51123 0.71 8 102462 1493776 TFCP2L3 0.53 8 103223 4945438 EDD 0.62 8 103797 1495112 AZIN1 0.58 8 103990 1495305 ATP6V1C1 0.56 8 104268 1495583 FZD6 0.47 8 104367 1495682 MFTC 0.53 8 104384 1495698 Gm83 0.62 8 109412 1500727 KIAA0103 0.55 8 110509 1501824 EBAG9 0.64 8 117614 1508929 EIF3S3 0.52 8 117815 1509130 RAD21 0.59 8 120700 1512015 TAF2 0.53 8 120803 1512118 DCC1 0.54 8 121365 1512680 MRPL13 0.61 8 121365 1512680 MRPL13 0.61 8 123984 1515299 DERL1 0.71 8 124114 1515429 MGC21654 0.63 8 124289 1515604 ATAD2 0.58 8 124386 1515700 FLJ10204 0.60 8 125420 1516735 FLJ20772 0.68 8 125444 1516759 RNF139 0.58 8 125968 1517283 SQLE 0.63 8 125993 1517308 KIAA0196 0.52 8 128705 1520020 MYC 0.51 8 128965 1520280 PVT1 0.66 8 120810 1522125 FAM498 0.68 8 132981 1524296 KIAA0143 0.60 8 133747 1525062 PHF20L1 0.60 8 134428 1525743 ST3GAL1 0.48 8 141504 1532819 EIF2C2 0.66 8 141640 1532954 PTK2 0.68 8 142403 1533718 PTP4A3 0.45 8 143807 1535122 JRK 0.54 8 143872 1535187 LOC51337 0.60 8 144479 1535794 FLJ14129 0.63 8 144554 1535869 MGC3113 0.54 8 144625 1535940 ZC3HDC3 0.68 8 144746 1536061 GSDMDC1 0.57 8 144767 1536082 EEF1D 0.68 8 144792 1536106 PYCRL 0.70 8 144800 1536115 TSTA3 0.52 8 144837 1536152 ZNF623 0.76 8 144979 1536294 SCRIB 0.78 8 145005 1536319 SIAHBP1 0.82 8 145046 1536361 EPPK1 0.45 8 145212 1536527 OPLAH 0.63 8 145240 1536554 EXOSC4 0.82 8 145244 1536558 GPAA1 0.70 8 145256 1536571 CYC1 0.74 8 145260 1536574 Sharpin 0.62 8 145443 1536758 LOC51236 0.72 8 145491 1536806 BOP1 0.71 8 145520 1536835 HSF1 0.78 8 145545 1536860 DGAT1 0.53 8 145584 1536899 FBXL6 0.72 8 145587 1536902 GPR172A 0.74 8 145623 1536938 CPSF1 0.68 8 145643 1536958 SLC39A4 0.59 8 145654 1536969 VPS28 0.75 8 145680 1536995 CYHR1 0.71 8 145742 1537057 RECQL4 0.51 8 145748 1537063 LRRC14 0.69 8 146003 1537318 ZNF34 0.72 8 146020 1537335 RPL8 0.73 8 146058 1537373 ZNF7 0.76 8 146111 1537626 ZNF250 0.66 8 146111 1537426 ZNF250 0.66 8 146161 1537475 ZNF16 0.67 8 146283 1537598 FLJ20989 0.68 9 701 1538325 ANKRD15 0.49 9 2005 1539629 SMARCA2 0.50 9 2794 1540418 KIAA0020 0.58 9 4670 1542293 CDC37L1 0.61 9 4783 1542407 RCL1 0.52 9 5348 1542972 C9orf46 0.55 9 6001 1543625 RANBP6 0.54 9 6748 1544372 JMJD2C 0.53 9 13097 1550720 MPDZ 0.50 9 15454 1553078 PSIP1 0.49 9 19039 1556663 RRAGA 0.46 9 19043 1556667 FAM29A 0.58 9 21321 1558945 KLHL9 0.61 9 21958 1559581 CDKN2A 0.45 9 32531 1570155 TOPORS 0.52 9 33912 1571536 UBAP2 0.60 9 35047 1572670 VCP 0.50 9 35064 1572668 FANCG 0.57 9 25595 1573219 TESK1 0.46 9 35722 1573346 CREB3 0.50 9 36181 1573805 CLTA 0.49 9 37419 1575042 GRHPR 0.52 9 75249 1612873 VPS13A 0.45 9 90401 1628025 NOLB 0.49 9 97364 1634988 SEC61B 0.53 9 96444 1636066 TEX10 0.45 9 121079 1658703 RABGAP1 0.61 9 122492 1660116 PSMB7 0.55 9 123008 1660631 ARPC5L 0.45 9 123017 1660640 GOLGA1 0.48 9 123287 1660911 PPP6C 0.50 9 123492 1661115 GAPVD1 0.56 9 123577 1661201 MAPKAP1 0.45 9 126304 1663928 CIZ1 0.46 9 127228 1664852 DOLPP1 0.46 9 130012 1667635 CSRP8 0.45 10 809 1674805 KIAA0217 0.65 10 5811 1679807 GDI2 0.46 10 11507 1685502 USP6NL 0.51 10 11768 1685784 ECHDC3 0.59 10 11966 1685962 UPF2 0.52 10 12176 1686171 SEC61A2 0.52 10 12242 1686238 C10orf7 0.559 10 12396 1686392 CAMK1D 0.48 10 13146 1687142 OPTN 0.54 10 13365 1687361 SEPHS1 0.61 10 14884 1688880 HSPA14 0.58 10 14954 1688950 DCLRE1C 0.49 10 15143 1689139 RPP338 0.53 10 26991 1700986 TPRT 0.48 10 30606 1704602 PAPD1 0.51 10 32304 1706300 KIF5B 0.48 10 34404 1706400 PARD3 0.48 10 70061 1744056 DDX21 0.47 10 70906 1744902 COL13A1 0.46 10 71920 1745916 SGPL1 0.48 10 74239 1748235 HSGT1 0.54 10 74279 1748275 TTC18 0.54 10 74337 1748333 KIAA0974 0.56 10 74346 1748342 DNAJC9 0.51 10 74355 1748351 MRPS16 0.61 10 74480 1748476 ANXA7 0.50 10 74541 1748537 PPP3CB 0.49 10 74851 1748847 SEC24C 0.60 10 74905 1748901 KIAA0913 0.61 10 74906 1748902 NDST2 0.59 10 74916 1748912 CAMK2G 0.64 10 75281 1749277 ADK 0.60 10 76315 1750311 VDAC2 0.62 10 80420 1754415 RAI17 0.54 10 80452 1754448 PPIF 0.51 10 81579 1755575 ANXA11 0.51 10 81879 1755874 TSPAN14 0.61 10 93879 1767874 IDE 0.47 10 97088 1771084 C10orf61 0.44 10 103270 1777266 C10orf76 0.44 10 105307 1779303 OBFC1 0.51 10 115259 1789255 DCLRE1A 0.47 10 118705 1792700 SLC18A2 0.46 10 123811 1797806 PLEKHA1 0.48 10 126065 1800061 KIAA0157 0.58 10 127099 1801905 DHX32 0.53 11 918 1809951 AP2A2 0.48 11 3794 1812827 FRAG1 0.54 11 6596 1815629 TAF10 0.63 11 10839 1819872 LOC58486 0.53 11 11828 1820861 USP47 0.57 11 11828 1820861 USP47 0.57 11 35649 1844682 TRIM44 0.54 11 36260 1845293 COMMD9 0.60 11 43345 1852378 TTC17 0.68 11 43667 1852700 HSD17B12 0.55 11 43844 1852877 DKFZ564C152 0.65 11 43885 1852918 DEPC-1 0.63 11 44081 1853114 EXT2 0.74 11 44545 1853578 TP53I11 0.69 11 44552 1853585 CD82 0.72 11 62102 1871135 EEF1G 0.46 11 64627 1873660 ZFPL1 0.45 11 64639 1873672 C11orf2 0.53 11 64725 1873758 CAPN1 0.45 11 64877 1873910 DPF2 0.51 11 66600 1875633 RHOD 0.49 11 66663 1875696 FBXL11 0.58 11 66809 1875842 ADRBK1 0.58 11 66941 1875974 PPP1CA 0.69 11 66971 1876004 RPS6KB2 0.52 11 66981 1876014 CORO1B 0.59 11 67007 1876040 FLJ21749 0.47 11 67026 1876059 AIP 0.57 11 67049 1876082 CDK2AP2 0.57 11 67150 1876183 NDIFV1 0.60 11 67205 1876238 ALDH3B2 0.54 11 67573 1876606 NDUFS8 0.67 11 67596 1876629 CHKA 0.48 11 68048 1877081 C11orf23 0.52 11 69229 1878262 CCND1 0.55 11 69776 1878809 FADD 0.81 11 69843 1878876 PPFIA1 0.80 11 69971 1879004 CTTN 0.82 11 70042 11879075 SHANK2 0.52 11 70872 1879905 DHCR7 0.67 11 70891 1879924 NADSYN1 0.71 11 71547 1770580 DKFZP564M082 0.53 11 71730 1880763 SKD3 0.48 11 72122 1881155 CENTD2 0.46 11 73310 1882343 E2IG2 0.49 11 73450 1882483 DKFZP586P0123 0.52 11 73609 1882642 PME-1 0.52 11 77054 1886087 CLNS1A 0.55 11 77104 1886137 HBXAP 0.67 11 77259 1886292 PTD015 0.72 11 77506 1886539 NDUFC2 0.49 11 77538 1886571 ALG8 0.48 11 93839 1902872 MRE11A 0.50 11 94488 11903521 SRP46 0.46 11 101805 1910838 PORIMIN 0.45 11 107418 1916451 CUL5 0.60 11 108073 1817106 DDX10 0.47 11 111011 1920044 SNF1LK2 0.48 11 111434 1920467 DLAT 0.51 11 111489 1920522 FLJ10726 0.63 11 111494 1920527 TIMM8B 0.46 11 111495 1920528 SDHD 0.53 11 111635 1920668 PTS 0.52 11 113142 1922175 ZW10 0.61 11 113809 1922842 RBM7 0.47 11 113848 1922881 DKFZP566E144 0.50 11 116244 1925277 APOA1 0.46 11 117810 1926843 ATP5L 0.56 11 117981 1927014 ARCN1 0.56 11 116424 1927457 RPS25 0.59 11 118505 1927538 DPAGT1 0.52 11 119746 1928779 ARHGEF12 0.48 11 123132 1932165 ANF202 0.47 11 123481 1933114 SPA17 0.47 11 124977 1934010 EI24 0.61 11 125000 1934033 ITM1 0.51 11 125301 1934334 PUS3 0.61 11 125670 1934703 SRPR 0.53 11 125711 1934744 DCPS 0.45 11 130283 1939316 SNX19 0.47 11 133656 1942689 THY28 0.59 12 264 1943780 JARID1A 0.56 12 733 1944249 WNK1 0.64 12 2837 1946353 POXM1 0.61 12 2870 1946386 TULP3 0.67 12 2939 1946455 TEAD4 0.59 12 4467 1947983 C12orf4 0.56 12 4570 1948085 DYRK4 0.52 12 4629 1948145 NDUFA9 0.65 12 6536 190052 NOL1 0.45 12 6630 1950146 ING4 0.45 12 6727 1950243 MLF2 0.49 12 6847 1950363 TPI1 0.51 12 6945 1950461 PHB2 0.52 12 6950 1950466 C2F 0.47 12 7234 1950750 PEX5 0.50 12 14831 1958347 WBP11 0.44 12 32723 1976239 DNM1L 0.46 12 32788 1976304 CGI-04 0.51 12 47510 1991026 DDX23 0.50 12 47537 1991053 RND1 0.45 12 48238 1991754 MCRS1 0.49 12 48433 1991948 TEGT 0.47 12 50676 1994192 ACVR1B 0.51 12 52122 1995638 DKFZp564J157 0.50 12 52161 1995677 MAP3K12 0.52 12 52181 1995697 TARPB2 0.52 12 54682 1998198 SUOX 0.48 12 56374 1999890 OS-9 0.55 12 56449 1999964 METTL1 0.48 12 62460 2005976 TMEM5 0.51 12 63395 2006911 GNS 0.47 12 63558 2007074 KIAA0984 0.55 12 63850 2007366 LEMD3 0.48 12 65949 2009465 TIP120A 0.61 12 66338 2009854 DYKR2 0.52 12 66975 2010491 MDM1 0.59 12 67367 2010883 NUP107 0.64 12 67426 2010942 SLC35E3 0.57 12 67488 2011004 MDM2 0.60 12 67525 2011041 MGC5370 0.65 12 68040 2011556 YEATS4 0.47 12 68266 2011781 CCT2 0.52 12 68958 2012474 CNOT2 0.52 12 74178 2017694 HRB2 0.53 12 105254 2048770 POLR3B 0.59 12 106629 2050145 PRDM4 0.45 12 107419 2050935 SART3 0.47 12 110691 2054207 FLJ39616 0.48 12 110663 2054379 C12orf8 0.46 12 112036 2055551 FLJ14827 0.48 12 118977 2062493 GCN1L1 0.53 12 119060 2062576 PXN 0.52 13 22102 2097697 MIPEP 0.47 13 28990 2104584 C13orf22 0.54 13 30889 2106483 PFAAP5 0.45 13 39101 2114696 MRPS31 0.51 13 39101 2114696 MRPS31 0.51 13 50505 2126099 NEK2 0.48 13 71128 2146722 KIAA1008 0.51 13 71156 2146750 C13orf24 0.54 13 77692 2153286 C13orf10 0.48 13 94152 2169746 UGCGL2 0.50 13 96304 2171899 RANBP5 0.46 13 96802 2172397 STK24 0.48 13 100947 2176542 TPP2 0.50 13 108992 2184586 FLJ12118 0.50 13 109065 2184660 ING1 0.46 13 109466 2185060 ARHGEF7 0.45 13 111087 2106682 TUBGCP3 0.50 13 112187 2187781 TFDP1 0.53 13 112918 2188513 CDC16 0.64 13 112965 2188560 UPF3A 0.52 14 18802 2207439 PARP2 0.50 14 19844 2208481 CHD8 0.56 14 19936 2208573 C14orf92 0.52 14 21226 2209863 OXA1L 0.47 14 29485 2218122 AP4S1 0.52 14 33021 2221658 SNX6 0.45 14 37726 2226364 CTAGE5 0.57 14 43575 2232212 FKBP3 0.54 14 48565 2237203 C14orf38 0.47 14 48574 2237211 SOS2 0.53 14 48703 2237341 C14orf160 0.69 14 48769 2237406 ATP5S 0.60 14 48925 2237563 MAP4K5 0.47 14 50446 2239084 C14orf166 0.64 14 50771 2239408 PTGER2 0.68 14 50771 2239408 PTGER2 0.68 14 51099 2239736 ERO1L 0.50 14 51164 2239801 PSMC6 0.63 14 53522 2242159 C14orf32 0.48 14 66108 2254745 VT11B 0.54 14 68224 2256861 SFRS5 0.46 14 71605 2260242 PSEN1 0.61 14 72344 2260981 ZNF410 0.63 14 72407 2261044 COQ6 0.48 14 72517 2261154 ALDH6A1 0.50 14 72742 2261379 ABCD4 0.50 14 73339 2261976 DLSTP 0.52 14 73539 2262178 NEK9 0.53 14 75778 2264415 GSTZ1 0.48 14 75883 2264520 C14orf133 0.54 14 76129 2264767 ALKBH 0.46 14 88853 2277491 CALM1 0.53 14 91396 22280034 ITPK1 0.51 14 92507 2281145 DDX24 0.46 14 94000 2282638 C14orf87 0.51 14 97854 2286492 C14orf154 0.57 14 98838 2287476 MGC4645 0.52 14 101389 2290026 CDC42BPB 0.51 14 102013 2290651 BAG5 0.59 14 102369 2291006 C14orf2 0.44 14 103207 2291845 AKT1 0.46 14 103314 2291952 KIAA02284 0.46 14 103830 2292467 PACS2 0.53 15 38776 2332725 FLJ10634 0.48 15 40167 2334116 VPS39 0.49 15 59681 2353809 VPS13C 0.46 15 62396 2356344 TRIP4 0.45 15 62682 2356631 ZNF609 0.45 15 63266 2357215 PARP16 0.48 15 63455 2357403 DPP8 0.48 15 63587 2357535 DKFZP564O1664 0.55 15 63878 2357826 RAB11A 0.57 15 64503 2358451 SNAPC5 0.53 15 64507 2358456 RPL4 0.55 15 64513 2358462 FLJ10036 0.51 15 66062 2360011 PIAS1 0.47 15 70482 2364431 ARIH1 0.48 15 72617 2366565 CLK3 0.48 15 73344 2367293 COMMD4 0.47 15 73475 2367424 PTPN9 0.48 15 73647 2367596 C15orf12 0.48 15 76304 2370252 REC14 0.65 15 86732 2380681 MRPL46 0.46 15 86740 2380689 MRPS11 0.50 15 86740 2380689 MRPS11 0.50 15 97991 2391940 MEF2A 0.58 15 99557 2393506 SNRPA1 0.54 15 99557 SNRPA1 0.54 15 99917 2393866 BLP2 0.59 16 37 2394242 POLR3K 0.48 16 48 2394253 RHBDF1 0.56 16 69 2394274 MPG 0.64 16 387 2394592 NME4 0.48 16 392 2394597 DECR2 0.61 16 560 2394765 PIGQ 0.48 16 658 2394863 RHIT2 0.58 16 670 2394876 STUB1 0.54 16 711 2394916 MGC2494 0.57 16 720 2394925 NARFL 0.47 16 844 2395049 FLJ12681 0.51 16 1483 2395689 KIAA0683 0.55 16 1500 2395706 KIAA0590 0.60 16 1668 2395873 C16orf34 0.51 16 1760 2395966 NME3 0.49 16 1974 2396179 GFER 0.50 16 2214 2396419 E4F1 0.48 16 2528 2396733 PDPK1 0.58 16 2822 2397027 TCEB2 0.49 16 3073 2397278 HCFC1R1 0.58 16 3179 2397384 LOC440334 0.49 16 3334 2397539 ZNF263 0.58 16 3432 2397638 ZNF434 0.56 16 3452 2397657 ZNF174 0.54 16 3508 2397714 FLJ14154 0.55 16 3560 2397765 CLUAP1 0.53 16 3706 2397914 TRAP1 0.54 16 3777 2397982 CREBBP 0.56 16 3777 2397982 CREBBP 0.56 16 4015 23962220 ADCY9 0.45 16 4391 2398596 Magmas 0.48 16 30698 2424903 MGC3121 0.46 16 30713 2424918 FBS1 0.48 16 31081 2425286 STX4A 0.48 16 31156 2425361 BCKDK 0.54 16 31165 2425370 MYST1 0.47 16 31179 2425384 PRSS8 0.45 16 31537 2425742 FLJ13868 0.64 16 46475 2440680 BPS35 0.49 16 47276 2441482 PHK8 0.48 16 48172 2442378 SIAH1 0.54 16 48354 2442560 N4BP1 0.54 16 53044 2447249 CHD9 0.50 16 53247 2447452 RBL2 0.48 16 57272 2451477 DOK4 0.59 16 57272 2451477 POLR2C 0.49 16 57547 2451752 KATNB1 0.56 16 57811 2452016 FLJ13154 0.46 16 57923 2452128 GTL3 0.50 16 57967 2452172 CSNK2A2 0.52 16 58325 2452530 FLJ21148 0.54 16 58330 2452535 CNOT1 0.62 16 58516 2452722 GOT2 0.46 16 66534 2460739 DNCLI2 0.61 16 66742 2460947 CGI-128 0.54 16 66840 2461045 CBFB 0.49 16 66964 2461170 TRADD 0.51 16 67037 2461242 HSPC171 0.50 16 67248 2461453 ATP6V0D1 0.45 16 67468 2461673 ACD 0.51 16 67485 2461690 MGC11335 0.45 16 67533 2461738 RANBP10 0.56 16 67652 22461858 THAP11 0.48 16 67657 2461862 NUTF2 0.57 16 67831 2462037 DDX28 0.55 16 67896 2462101 NFATC3 0.49 16 68077 2462282 SLC7A6 0.49 16 69121 2463327 VPS4A 0.49 16 69572 2463778 WWP2 0.47 16 70063 2464268 AARS 0.52 16 70157 2464362 DDX19L 0.57 16 70334 2464539 SFB3B3 0.54 16 70498 2464703 VAC14 0.54 16 71541 2465746 AP1G1 0.53 16 71706 2465911 KIAA0174 0.59 16 71904 2466109 DHX38 0.66 16 74110 2468315 PSMD7 0.51 16 74266 2468471 GLG1 0.50 16 74435 2468640 RFWD3 0.53 16 75107 2469312 CFDP1 0.48 16 75441 2469646 KARS 0.64 16 75461 2469666 TERF2IP 0.53 16 77005 2471210 MON1B 0.62 16 80789 2474994 DC13 0.54 16 80854 2475059 KIAA0431 0.51 16 83621 2477827 HSBP1 0.53 16 83712 2477918 MLYCD 0.47 16 83867 2478072 MBTPS1 0.46 16 82991 2478196 TAF1C 0.446 16 84293 2478496 KIAA1609 0.48 16 84462 2478667 C16orf44 0.46 16 84513 2478718 USP10 0.70 16 84788 2478993 ZDHHC7 0.50 16 85594 2479799 NOC4 0.56 16 85615 2479820 COX411 0.66 16 85346 2480551 FLJ12998 0.51 16 87214 2481419 MAP1LC3B 0.49 16 88620 2482825 APRT 0.48 16 88668 2482873 HSPC176 0.54 16 89095 2483300 ANKRD11 0.54 16 89371 2483576 LOC388344 0.55 16 89684 2483889 KIAA1049 0.54 16 89757 2483963 FLJ20186 0.54 17 670 2484917 RNMTL1 0.47 17 1761 2486008 PRPF8 0.56 17 2140 2486387 DPH2L1 0.48 17 2433 2486680 FLJ10534 0.48 17 2433 2486680 SRR 0.58 17 2704 2486951 PAFAH1B1 0.52 17 7324 2491571 ACADVL 0.58 17 7348 2491595 DULLARD 0.46 17 7417 2491664 GPS2 0.46 17 7494 2491741 PLSCR3 0.47 17 16049 2500296 ADORA2B 0.48 17 16103 TTC19 0.46 17 17269 2501516 M-RIP 0.52 17 18349 2502596 FLII 0.54 17 18962 2503209 PRPSAP2 0.45 17 19303 2503550 EPN2 0.49 17 19444 2503691 MAPK7 0.55 17 21192 2505439 DKFZp566O084 0.48 17 21263 2505510 C17orf35 0.46 17 21357 2505604 MAP2K3 0.45 17 28046 2512293 GIT1 0.52 17 28852 2513099 CPD 0.49 17 28950 2513197 GOSR1 0.60 17 30336 2514583 HCA66 0.52 17 30410 2514657 LOC440423 0.59 17 30615 2514862 RHOT1 0.47 17 30804 2515051 MNJMU-R1 0.61 17 30917 2515164 PSMD11 0.47 17 30960 2515207 CDK5R1 0.65 17 30960 2515207 CDK5R1 0.65 17 32743 2516990 CCL7 0.61 17 33400 2517648 CCT6B 0.47 17 33453 2517700 LIG3 0.63 17 33573 2517820 RAD51L3 0.71 17 33604 2517851 FLJ10458 0.67 17 38168 2522415 STARD3 0.86 17 28197 2522444 TCAP 0.55 17 38199 2522447 PNMT 0.54 17 38202 2522449 PERLD1 0.88 17 38231 2522478 ERBB2 0.86 17 38269 2522516 GRB7 0.84 17 38448 2522695 GSDML 0.66 17 38512 2522759 PSMD3 0.73 17 38550 2522797 THRAP4 0.77 17 38550 2522797 THRAP4 0.77 17 38672 2522919 CASC3 0.58 17 38792 25223039 WIRE 0.57 17 39158 2523405 SMARCE1 0.57 17 39158 2523405 SMARCE1 0.57 17 39348 2523595 KRT10 0.70 17 41087 2525334 COASY 0.49 17 41092 2525339 MLX 0.53 17 41225 2525473 EZH1 0.50 17 41359 2525606 PSME3 0.51 17 41476 2525723 MGC2744 0.49 17 41551 2525798 RND2 0.44 17 41696 2525943 MBR1 0.48 17 42036 2526284 DHX4 0.48 17 43614 2527861 NMT1 0.57 17 43990 2528237 PLEKHM1 0.52 17 44067 2528315 LOC9884 0.49 17 46494 2530471 PNPO 0.44 17 47445 2531692 ATP5G1 0.60 17 47461 2531706 FLJ13855 0.69 17 47482 2531729 EAP30 0.78 17 47847 2532094 ZNF652 0.50 17 47956 2532203 PHB 0.64 17 48152 2532399 SPCP 0.55 17 48253 2532500 SLC35B1 0.79 17 48341 2532588 MYST2 0.78 17 48525 2532772 DLX4 0.67 17 48808 2532855 ITGA3 0.58 17 48647 2532894 PDK2 0.49 17 48896 2533145 XYLT2 0.74 17 48933 2533180 PRO1855 0.50 17 48978 2533225 FLJ20920 0.45 17 49031 2533278 RSAD1 0.63 17 49031 2533332 EPN3 0.55 17 49099 2533346 SSP411 0.57 17 49247 2533494 MGC15396 0.46 17 49272 2533519 CROP 0.68 17 49414 2533661 TOB1 0.54 17 49518 2533765 SPAG9 0.59 17 49706 2533953 NME1 0.59 17 49718 2533966 NME2 0.72 17 55386 2539633 DGKE 0.65 17 55490 2539737 COIL 0.86 17 55637 2539884 AKAP1 0.69 17 56757 2541055 FLJ20345 0.48 17 56897 2541144 SUPT4H1 0.58 17 56906 2541153 FLJ20315 0.64 17 57042 2541289 MTMR4 0.47 17 57109 2541356 TEX14 0.49 17 57245 2541492 RAD51C 0.77 17 57550 2541797 TRIM37 0.66 17 57762 2542009 FLJ10587 0.73 17 58117 2542364 DHX40 0.71 17 58172 2542419 CLTC 0.70 17 58249 2542496 BIT1 0.78 17 58249 2542496 BIT1 0.78 17 58259 2542507 VMP1 0.52 17 58412 2542659 TUBD1 0.65 17 58445 2542692 RPS6KB1 0.69 17 58445 2542692 RPS6KB1 0.69 17 58504 2542751 LOC51136 0.52 17 58595 2542842 ABC1 0.79 17 58731 2542978 USP32 0.70 17 58995 2543242 APPBP2 0.78 17 59152 2543399 PPM1D 0.59 17 59230 2543477 BCAS3 0.61 17 60234 2544482 BCAS3 0.61 17 60234 2544482 BRIP1 0.50 17 60497 2544745 THRAP1 0.70 17 61030 2542577 TLK2 0.77 17 61940 2546187 DKFZP564D166 0.59 17 61984 2546231 CYB561 0.61 17 62142 2546389 HAN11 0.49 17 62254 2546501 LYK5 0.56 17 62343 2546590 DDX42 0.52 17 62370 2546617 FTSJ3 0.52 17 62383 2546630 SMARCD2 0.48 17 65578 2549826 CACNG4 0.64 17 65624 2549871 HELZ 0.63 17 53884 2550131 PSMD12 0.55 17 65924 2550171 PITPNC1 0.50 17 66264 2550511 DKFZP586L0724 0.51 17 71763 2556010 SSTR2 0.65 17 71877 2556124 CDC42EP4 0.52 17 73033 2557280 GPRC5C 0.48 17 73364 2557611 EBSP 0.63 17 73370 2557617 FLJ20255 0.62 17 73456 2557703 FDXR 0.45 17 73581 2557828 HUMPPA 0.65 17 73606 2557853 ICT1 0.73 17 73632 2557879 ATP5H 0.59 17 73640 2557888 KCTD2 0.66 17 73681 2557928 SLC16A5 0.59 17 73703 2557950 ARMC7 0.64 17 73729 2557976 HN1 0.50 17 73761 2558008 SUMO2 0.59 17 73799 2558046 PCNT1 0.57 17 73830 2558077 GGA3 0.64 17 73855 2558102 MRPS7 0.59 17 73911 2558158 GRB2 0.60 17 74050 2558297 KIAA0195 0.66 17 74093 2558341 CASKIN2 0.66 17 744093 2558341 CASKIN2 0.66 17 74119 2558366 LLGL2 0.54 17 74220 2558467 RECQL5 0.64 17 74261 2558508 HCNGP 0.56 17 74439 2558686 WBP2 0.49 17 74600 2558847 EVPL 0.57 17 74677 2558924 EXOC7 0.59 17 74983 2559230 E2-230K 0.71 17 75064 2559311 RHBDL6 0.47 17 75913 2560160 9-Sep 0.58 17 76706 2560953 EVER1 0.54 17 76762 2561009 SYNGR2 0.51 17 76807 2561055 BIRC5 0.48 17 76972 2561219 PGS1 0.66 17 77267 2561514 PSCD1 0.49 17 79709 2563956 BAIAP2 0.49 17 79864 2564111 AZI1 0.44 17 81095 2565343 NARF 0.49 17 81156 2565404 FOXK2 0.50 17 81251 2565498 WDR45L 0.55 17 81294 2565541 RAB40B 0.56 17 81353 2565601 FM3KRP 0.53 18 149 2566256 USP14 0.48 18 205 22566312 THOC1 0.52 18 712 2566819 YES1 0.57 18 2528 2568636 METTL4 0.57 18 2562 2568669 KNTC2 0.45 18 2793 2568900 SMCHD1 0.57 18 3252 2569360 MRLC2 0.52 18 9093 2575200 NDUFV2 0.51 18 9172 2575280 ANKRD12 0.57 18 9466 2575573 RALBP1 0.64 18 9537 2575644 PP4R1 0.53 18 10516 2576623 NAPG 0.47 18 11841 2577949 CHMP1B 0.62 18 11874 2577981 MPPE1 0.57 18 11971 2578079 IMPA2 0.60 18 12298 2578406 TUBB6 0.51 18 12319 2578426 AFG3L2 0.73 18 12422 2578529 C18ord43 0.45 18 12663 2578770 C18orf9 0.55 18 12693 2578800 TNFSG5IP1 0.63 18 12783 2578891 PTPN1 0.57 18 12938 2579045 SEH1L 0.69 18 13717 2579824 RNMT 0.68 18 19335 2585443 C18orf8 0.51 18 19363 2585471 NPC1 0.52 18 20259 2586366 IMPACT 0.47 18 27662 2593770 KIAA1012 0.53 18 31122 2597230 ZNF271 0.45 18 32626 2598735 C18orf10 0.54 18 37787 2603895 PIK3C3 0.48 18 43620 2609727 SMAD2 0.59 18 45267 2611374 RPL17 0.46 18 46047 2612155 MBD1 0.58 18 46061 2612168 CXXC1 0.55 18 57860 2623968 PIGN 0.50 18 58391 2624498 ZCCHC2 0.51 18 585333 2624640 PHLPP 0.57 18 59147 2625254 FVT1 0.76 18 59205 2625313 VPS48 0.49 18 75761 2641869 PQLC1 0.63 18 75832 2641939 TXNL4A 0.63 18 75893 2642001 C18orf22 0.60 18 75966 2642074 KIAA0863 0.49 19 12673 2654896 TNPO2 0.46 19 15325 2657548 AKAP8 0.57 19 15394 2657616 WIZ 0.52 19 17309 2659352 GTPBP3 0.59 19 17483 2659706 PGLS 0.50 19 18804 2661026 RENT1 0.48 19 18891 2861114 HOMER3 0.54 19 18892 2661114 DDX49 0.57 19 19091 2661314 FLJ20422 0.54 19 19317 2661540 KIAA0892 0.47 19 34390 2676613 UQCRFS1 0.54 19 34789 2677012 POP4 0.67 19 34848 2677070 PLENKF1 0.55 19 34995 2677217 CCNE1 0.67 19 35125 2677348 C19orf2 0.73 19 40812 2683034 MGC10433 0.46 19 42815 2685038 KIAA0961 0.48 19 43802 2666024 EIF2S12 0.57 19 43830 2686053 ACTN4 0.47 19 48792 2691015 ANF576 0.45 19 50575 2692797 PPP1R13L 0.46 19 50605 2692827 ERCC1 0.54 19 54309 2696532 LIN7B 0.49 19 54641 2696864 FLJ20643 0.78 19 54683 2696905 RPL13A 0.49 19 54708 2696931 FCGRT 0.52 19 54751 2696973 NOSIP 0.71 19 54778 2697001 PRRG2 0.53 19 54855 2697077 IRF3 0.54 19 55013 2697236 MED25 0.45 19 55046 2697269 PTOV1 0.47 19 55056 2697279 PNKP 0.51 19 55073 26972295 TBC1D17 0.44 19 55102 2697324 NUP62 0.57 19 55172 2697394 VRK3 0.56 19 55221 2697444 ZNF473 0.52 19 60433 2702655 KIAA1115 0.51 19 60465 2702668 HSPBP1 0.49 19 60656 2702879 ISOC2 0.54 19 60845 2703067 ZNF580 0.45 19 62554 2704777 ZNF304 0.50 19 622691 2704913 ZNF419 0.61 19 622817 2705040 ZNF134 0.50 19 62885 2705108 ZNF551 0.52 19 63386 2705609 ZNF274 0.44 19 63482 2705705 ZNF8 0.47 19 63661 2705883 FLJ45850 0.49 19 63670 2705893 ZNF324 0.65 19 63748 2705970 TRIM28 0.49 19 63759 2705981 UBE2M 0.44 20 459 2706493 CSNK2A1 0.48 20 1418 2707452 NSFL1C 0.45 20 3185 2709219 ITPA 0.48 20 20010 2726044 CRNKL1 0.444 20 32662 2738696 CDKSRAP1 0.50 20 34018 2740052 NCOA6 0.73 20 34232 2740266 GSS 0.69 20 34306 2740340 TRPC4AP 0.73 20 34419 2740453 C2orf31 0.49 20 34530 2740564 LOC400843 0.65 20 34582 2740616 ITGB4BP 0.67 20 34606 2740640 C20orf44 0.59 20 34759 2740793 CEP2 0.72 20 34919 2740953 SPAG4 0.48 20 34929 2740964 CPNE1 0.66 20 34998 2741032 NFS1 0.71 20 35007 2741041 PRNC2 0.60 20 35105 2741139 PHF20 0.68 20 35257 2741291 SCAND1 0.73 20 35458 2741492 EPB41L1 0.68 20 35540 2741574 C20orf4 0.79 20 35681 2741715 DLGAP4 0.67 20 35920 2741954 C20orf24 0.54 20 35966 2742000 NDRG3 0.68 20 36066 2742100 C20orf172 0.48 20 36105 2742139 KIAA0889 0.70 20 36493 2742527 RPN2 0.67 20 38063 2744097 ACTR5 0.68 20 38276 2744311 DHX35 0.70 20 40419 2746453 ZHX3 0.51 20 40452 2746486 PLCG1 0.45 20 43511 2749545 C20orf111 0.69 20 43814 2749848 C20orf121 0.69 20 43814 2749848 TDE1 0.54 20 43846 2749880 PKIG 0.54 20 44200 2750234 YWHAB 0.51 20 44256 2750290 TOMM34 0.58 20 45156 2751190 PTE1 0.52 20 45206 2751240 PPGB 0.45 20 45228 2751262 SLC12A5 0.47 20 46816 2752850 NCOA3 0.54 20 48224 2754258 ARGEF2 0.65 20 48348 2754382 CSE1L 0.67 20 48415 2754450 STAU 0.69 20 48521 2754555 DDX27 0.78 20 48935 2754969 B4GALT5 0.48 20 49148 2755183 SLC9A8 0.73 20 49205 2755240 SPATA2 0.77 20 49238 2755273 ZNF313 0.46 20 49383 2755417 UBE2V1 0.76 20 49812 2755846 PTPN1 0.49 20 50020 2756054 PARD6B 0.52 20 50237 2756271 DPM1 0.49 20 50261 2756295 MOCS3 0.56 20 50899 2756933 ATP9A 0.52 20 51453 2757487 ZFP64 0.53 20 52869 2758903 ZNF217 0.50 20 53510 2759544 PFDN4 0.71 20 55653 2761687 CSTF1 0.56 20 55729 2761763 C20orf43 0.67 20 55890 2761924 TFAP2C 0.51 20 56612 2762646 RAE1 0.76 20 56619 2762654 RNPC1 0.45 20 56822 2762856 PCK1 0.58 20 57627 2763662 RAB22A 0.72 20 57650 2763684 VAPB 0.75 20 57912 2753947 STX16 0.82 20 57953 2763987 NPEPL1 0.69 20 63054 2769098 ARFRP1 0.60 20 63093 2769127 ZGPAT 0.58 20 63098 2769132 SLC2A4RG 0.50 20 63223 2769257 TPD52L2 0.57 20 63223 2769257 TPD52L2 0.57 20 63298 2769332 UCKL1 0.53 20 63339 2769373 C20orf14 0.50 20 63431 2769465 TGS19 0.45 21 26030 2795806 GABPA 0.48 21 33836 2803612 SON 0.46 21 36665 2806441 ZCWCC3 0.48 21 36679 2806455 CHAF1B 0.50 21 44383 2814160 PWP2H 0.51 21 44410 2814186 C21orf33 0.44 21 45045 2814821 UBE2G2 0.52 21 45082 2814858 SUMO3 0.49 21 45126 2814902 PTTG1IP 0.47 21 46600 2816376 PCNT2 0.56 21 46912 2816688 HRMT1L1 0.53 22 15993 2832745 CECR5 0.48 22 16546 2833298 BCL2L13 0.48 22 16546 2833298 BCL2L13 0.48 22 16645 2833397 MICAL3 0.47 22 16935 2833687 PEX26 0.50 22 17496 2834248 DGCR14 0.50 22 17693 2834445 HIRA 0.45 22 17813 2834565 UFD1L 0.54 22 18304 2835056 COMT 0.55 23 18480 2835232 RANBP1 0.58 22 19120 2835873 KELCHL 0.47 22 19538 2836290 SNAP29 0.45 22 26492 2842344 MN1 0.70 22 27408 2844160 CHEK2 0.52 22 28048 2844800 AP1B1 0.48 22 28226 2844979 C22ord19 0.66 22 28275 2845027 NIPSNAP1 0.48 22 28488 2845240 UCRC 0.47 22 2927 2846049 PES1 0.64 22 29566 2846318 ZCWCC1 0.49 22 26692 2846444 FLJ20618 0.51 22 29969 2846721 LIMK2 0.61 22 30120 2846872 DRG1 0.68 22 30475 2847228 DEPDC5 0.46 22 31108 2847860 HSPC117 0.56 22 31195 2847947 FBXO7 0.54 22 35087 2851839 FLJ23322 0.50 22 35106 2851858 TXN2 0.45 22 35150 2851902 EIF3S7 0.54 22 36200 2852952 CDC42EP1 0.50 22 36488 2853241 EIF3S6IP 0.53 22 36488 2853241 EIF3S6IP 0.53 22 36551 2853304 MICAL-1 0.52 22 36593 2853345 POLR2F 0.47 22 37267 2854019 GTPBP1 0.557 22 37325 2854077 KIAA0063 0.52 22 37621 2854374 APOBEC3B 0.49 22 38003 2854755 SYNGR1 0.52 22 38141 2854894 FLJ20232 0.55 22 38962 2855714 TNRC6B 0.45 22 338966 2855738 ADSL 0.49 22 29464 2856216 ST13 0.53 22 39590 2856343 RBX1 0.45 22 40108 2856860 ACO2 0.46 22 40136 2856888 D15Wsu75e 0.60 22 40260 2857013 G22P1 0.56 22 40313 2857065 NHP2L1 0.61 22 40472 2857224 SREBF2 0.51 22 40725 2857477 NDUFA6 0.46 22 40767 2857519 CYP2D6 0.51 22 40787 2857539 TCF20 0.53 22 41166 2857918 DIA1 0.60 22 41497 2858249 PACSIN2 0.47 22 41778 2858531 BZRP 0.49 22 42610 2859362 CGI-51 0.49 22 43860 2860612 NUP50 0.65 22 45293 2862045 CERK 0.48 22 48386 2865138 BRD1 0.56 22 48467 2865219 ZBED4 0.58 22 48532 2865284 MGC11256 0.63 22 48806 2865559 PP2447 0.52 22 48895 2865648 PLXNB2 0.52 22 49002 2866754 KIAA0685 0.58 22 49018 2865770 SBF1 0.55 22 49071 2865823 ARSA 0.56 22 49074 2865826 BC002942 0.54 22 49079 2865831 384D8-2 0.55 22 49094 2865847 SCO2 0.59 22 49097 2865849 ECGF1 0.47 22 49099 2865851 LOC440836 0.49 X 43779 2909928 UTX 0.50 X 71910 2938059 XIST 0.67 XY 114 3019955 GTPBP6 0.50 XY 1150 3020992 SLC25A6 0.46 XY 1168 3021009 ASMTL 0.49 XY 2000 3021841 ZBED1 0.45

TABLE 8 si RNA sequences for selected 11q13 amplicon genes siRNA duplex no. Gene sense/antisense sequence CCND1 1--sense ACAACUUCCUGUCCUACUAUU (SEQ ID NO: 142) CCND1 1--antisense 5′-PUAGUAGGACAGGAAGUUGUUU (SEQ ID NO: 143) CCND1 2--sense GUUCGUGGCCUCUAAGAUGUU (SEQ ID NO: 144) CCND1 2--antisense 5′-PCAUCUUAGAGGCCACGAACUU (SEQ ID NO: 145) CCND1 3--sense GCAUGUAGUCACUUUAUAAUU (SEQ ID NO: 146) CCND1 3--antisense 5′-PUUAUAAAGUGACUACAUGCUU (SEQ ID NO: 147) CCND1 4--sense GCGUGUAGCUAUGGAAGUUUU (SEQ ID NO: 148) CCND1 4--antisense 5′-PAACUUCCAUAGCUACACGCUU (SEQ ID NO: 149) FGF3 1--sense GAGCUGGGCUAUAAUACGUUU (SEQ ID NO: 150) FGF3 1--antisense 5′-PACGUAUUAUAGCCCAGCUCUU (SEQ ID NO: 151) FGF3 2--sense GGCGGUACCUGGCCAUGAAUU (SEQ ID NO: 152) FGF3 2--antisense 5′-PUUCAUGGCCAGGUACCGCCUU (SEQ ID NO: 153) FGF3 3--sense GCGCCGAGAGACUGUGGUAUU (SEQ ID NO: 154) FGF3 3--antisense 5′-PUACCACAGUCUCUCGGCGCUU (SEQ ID NO: 155) FGF3 4--sense AGAAGCAGAGCCCGGAUAAUU (SEQ ID NO: 156) FGF3 4--antisense 5′-PUUAUCCGGGCUCUGCUUCUUU (SEQ ID NO: 157) PPFIA1 1--sense GAAGAAAGGUUACGACAGAUU (SEQ ID NO: 158) PPFIA1 1--antisense 5′-PUCUGUCGUAACCUUUCUUCUU (SEQ ID NO: 159) PPFIA1 2--sense GAGUAGCACUUGAAAGAUGUU (SEQ ID NO: 160) PPFIA1 2--antisense 5′-PCAUCUUUCAAGUGCUACUCUU (SEQ ID NO: 161) PPFIA1 3--sense AGACAACCAUAAAGUGUGAUU (SEQ ID NO: 162) PPFIA1 3--antisense 5′-PUCACACUUUAUGGUUGUCUUU (SEQ ID NO: 163) PPFIA1 4--sense AAAGGACAUUCGUGGCUUAUU (SEQ ID NO: 164) PPFIA1 4--antisense 5′-PUAAGCCACGAAUGUCCUUUUU (SEQ ID NO: 165) FOLR3 1--sense GGACGGACCUGCUCAAUGUUU (SEQ ID NO: 166) FOLR3 1--antisense 5′-PACAUUGAGCAGGUCCGUCCUU (SEQ ID NO: 167) FOLR3 2--sense GAAUUGGACCUCAGGGAUUUU (SEQ ID NO: 168) FOLR3 2--antisense 5′-PAAUCCCUGAGGUCCAAUUCUU (SEQ ID NO: 169) FOLR3 3--sense UAACUGGGAUCACUGUGGUUU (SEQ ID NO: 170) FOLR3 3--antisense 5′-PACCACAGUGAUCCCAGUUAUU (SEQ ID NO: 171) FOLR3 4--sense UCUCGUGGGAUUAUUGAUUUU (SEQ ID NO: 172) FOLR3 4--antisense 5′-PAAUCAAUAAUCCCACGAGAUU (SEQ ID NO: 173) NEU3 1--sense ACUGGAUAAUAGUGCGUAUUU (SEQ ID NO: 174) NEU3 1--antisense 5′-PAUACGCACUAUUAUCCAGUUU (SEQ ID NO: 175) NEU3 2--sense GCAGAGAAGCGUUCCACGAUU (SEQ ID NO: 176) NEU3 2--antisense 5′-PUCGUGGAACGCUUCUCUGCUU (SEQ ID NO: 177) NEU3 3--sense GAGCUGAGUUGGCGAGGGUUU (SEQ ID NO: 178) NEU3 3--antisense 5′-PACCCUCGCCAACUCAGCUCUU (SEQ ID NO: 179) NEU3 4--sense CUCAUUAGGCCCAUGGUUAUU (SEQ ID NO: 180) NEU3 4--antisense 5′-PUAACCAUGGGCCUAAUGAGUU (SEQ ID NO: 181)

TABLE 9 Sequence of shRNAs targeting human NEU3, FGF3 and PPFIA1 genes gene shRNA clone Sequence NEU3 Hairpin sequence for TRCN0000005149 CCGGAGTGACAACATGCTCCTTCAACTCGAGTTGAAGGAGCATGTTGTCACTTTTTT (SEQ ID NO: 182) NEU3 Hairpin sequence for TRCN0000005148 CCGGCCGAGCTACAGACCAATATAACTCGAGTTATATTGGTCTGTAGCTCGGTTTTT (SEQ ID NO: 183) NEU3 Hairpin sequence for TRCN0000005147 CCGGCCCTGCGTATACCTACTACATCTCGAGATGTAGTAGGTATACGCAGGGTTTTT (SEQ ID NO: 184) NEU3 Hairpin sequence for TRCN0000005146 CCGGCCTACCTTCTACAGCCTTGTACTCGAGTACAAGGCTGTAGAAGGTAGGTTTTT (SEQ ID NO: 185) NEU3 Hairpin sequence for TRCN0000010926 CCGGGCAGAAGAGTGGTTGTGTGTTCTCGAGAACACACAACCACTCTTCTGCTTTTT (SEQ ID NO: 186) FGF3 Hairpin sequence for TRCN0000038162 CCGGCAAGCTCTACTGCGCCACGAACTCGAGTTCGTGGCGCAGTAGAGCTTGTTTTTG (SEQ ID NO: 187) FGF3 Hairpin sequence for TRCN0000038159 CCGGACTCTATGCTTCGGAGCACTACTCGAGTAGTGCTCCGAAGCATAGAGTTTTTTG (SEQ ID NO: 188) FGF3 Hairpin sequence for TRCN0000038161 CCGGCGGCAGAAGCAGAGCCCGGATCTCGAGATCCGGGCTCTGCTTCTGCCGTTTTTG (SEQ ID NO: 189) FGF3 Hairpin sequence for TRCN0000038160 CCGGGAGCTGGGCTATAATACGTATCTCGAGATACGTATTATAGCCCAGCTCTTTTTG (SEQ ID NO: 190) FGF3 Hairpin sequence for V2LHS_43059 TGCTGTTGACAGTGAGCGCCCACGAGCTGGGCTATAATACTAGTGAAGCCACAGATGTA (mir-30) GTATTATAGCCCAGCTCGTGGATGCCTACTGCCTCGGA (SEQ ID NO: 191) PPFIA1 Hairpin sequence for TRCN0000002969 CCGGGAGGAGATTGAAAGTCGAGTTCTCGAGAACTCGACTTTCAATCTCCTCTTTTT (SEQ ID NO: 192) PPFIA1 Hairpin sequence for TRCN0000002967 CCGGGTAGTTTGTTAGAAGAGGAATCTCGAGATTCCTCTTCTAACAAACTACTTTTT (SEQ ID NO: 193) PPFIA1 Hairpin sequence for TRCN0000002968 CCGGGCTCCAAGAAATCATAAGTAACTCGAGTTACTTATGATTTCTTGGAGCTTTTT (SEQ ID NO: 194) PPFIA1 Hairpin sequence for TRCN0000002971 CCGGGCACAGTTGGAGGAGAAGAATCTCGAGATTCTTCTCCTCCAACTGTGCTTTTT (SEQ ID NO: 195) PPFIA1 Hairpin sequence for TRCN0000002970 CCGGGCATATTAACAAGCCAGCAAACTCGAGTTTGCTGGCTTGTTAATATGCTTTTT (SEQ ID NO: 196) PPFIA1 Hairpin sequence for V2LHS_27777 TGCTGTTGACAGTGAGCGCCCTTGAAAGGGAAGAAGAAATTAGTGAAGCCACAGATGTA (mir-30) ATTTCTTCTTCCCTTTCAAGGATGCCTACTGCCTCGGA (SEQ ID NO: 197) The linker sequences connecting the sense and anti-sense sequences are bolded.

TABLE 10 Target si RNA sequences for selected 20q13 amplicon genes siRNA Gene no. Target sequence BCAS1 1 UAUCAGGGCAGUCCGAUGA (SEQ ID NO: 198) BCAS1 2 GAAGUAGAAUCAGCCUUAC (SEQ ID NO: 199) BCAS1 3 GAUAAGUGCUGUUGCGGAU (SEQ ID NO: 200) BCAS1 4 CCAAUAAAGCUCCAGCGAA (SEQ ID NO: 201) CSTF1 1 GGAAAUAUCAACGGGACGA (SEQ ID NO: 202) CSTF1 2 GGAUGGUGUUUCAAAUCGA (SEQ ID NO: 203) CSTF1 3 CGAAUGAUUAGUAUCGAUU (SEQ ID NO: 204) CSTF1 4 GUAUGCAAUUGGUCGUUCA (SEQ ID NO: 205) PCK1 1 CCCAAGAUCUUCCAUGUCA (SEQ ID NO: 206) PCK1 2 CCAUGUACGUCAUCCCAUU (SEQ ID NO: 207) PCK1 3 GAAGUGCUUUGCUCUCAGG (SEQ ID NO: 208) PCK1 4 GGUGGAAGGUUGAGUGCGU (SEQ ID NO: 209) TMEPAI 1 GAAAGGACACCCUCUCUAG (SEQ ID NO: 210) TMEPAI 2 GCACUUGUAAAGAUGAUUA (SEQ ID NO: 211) TMEPAI 3 UCACUUAAGAGGCCAAUAA (SEQ ID NO: 212) TMEPAI 4 GCGCAGAAUUCUUCACCUU (SEQ ID NO: 213) RAB22A 1 GGACUACGCCGACUCUAUU (SEQ ID NO: 214) RAB22A 2 GAAGAAUUCCAUCCACUGA (SEQ ID NO: 215) RAB22A 3 GAAACAACCUCUGCGAAUU (SEQ ID NO: 216) RAB22A 4 GCAGUUUGAUUAUCCGAUU (SEQ ID NO: 217) VAPB 1 UGUUACAGCCUUUCGAUUA (SEQ ID NO: 218) VAPB 2 CCACGUAGGUACUGUGUGA (SEQ ID NO: 219) VAPB 3 GCUCUUGGCUCUGGUGGUU (SEQ ID NO: 220) VAPB 4 GUAAUUAUUGGGAAGAUUG (SEQ ID NO: 221) STX16 1 GUAUGAUGUUGGCCGGAUU (SEQ ID NO: 222) STX16 2 AAAUAUUCCACCAGCGAUU (SEQ ID NO: 223) STX16 3 GCUAAUUGAGAGAGCGUUA (SEQ ID NO: 224) STX16 4 AGUAGGACUUCAUCGUUUA (SEQ ID NO: 225) GNAS 1 GCAAGUGGAUCCAGUGCUU (SEQ ID NO: 226) GNAS 2 GCAUGCACCUUCGUCAGUA (SEQ ID NO: 227) GNAS 3 AUGAGGAUCCUGCAUGUUA (SEQ ID NO: 228) GNAS 4 CAACCAAAGUGCAGGACAU (SEQ ID NO: 229) 

1. A method for prognosing the outcome of a patient with breast cancer, said method comprising: providing breast cancer tissue from the patient; determining from the provided tissue, the level of gene amplification or gene expression product for at least one gene set forth in Table 3; identifying that the at least one gene or gene product is amplified; whereby, when the at least one gene or gene product is amplified, this is an indication that the patient has the predicted disease free survival or probability for distant recurrence set forth in Table3.
 2. The method of claim 1, wherein the gene or gene product is ACACA (SEQ ID NOs: 1, 2), ADAM9 (SEQ ID NOs: 3-8), ERBB2 (SEQ ID NOs: 9-14), FGFR1 (SEQ ID NOs: 15, 16), FNTA (SEQ ID NOs: 17, 18), IKBKB (SEQ ID NOs: 19, 20), NR1D1 (SEQ ID NOs: 21, 22), PNMT (SEQ ID NOs: 23, 24), or PROSC (SEQ ID NOs: 25, 26).
 3. The method of claim 2, wherein the gene or gene product is PROSC (SEQ ID NOs: 25, 26), ADAM9 (SEQ ID NOs: 3-8), FNTA (SEQ ID NOs: 17, 18), ACACA (SEQ ID NOs: 1, 2), PNMT (SEQ ID NOs: 23, 24), or NR1D1 (SEQ ID NOs: 21, 22).
 4. The method of claim 1, with a proviso that the gene or gene product is not ERBB2(SEQ ID NOs: 9-14), FGFR1 (SEQ ID NOs: 15, 16), or IKBKB (SEQ ID NOs: 19, 20).
 5. The method of claim 1, wherein the gene or gene product is ADAM9 (SEQ ID NOs: 3-8).
 6. The method of claim 1, wherein the gene or gene product is FGF3 (SEQ ID NOs: 65, 66), PPFIA1 (SEQ ID NOs: 69, 70), or NEU3 (SEQ ID NOs: 79, 80).
 7. The method of claim 6, wherein the breast cancer is a luminal A breast cancer.
 8. The method of claim 1, wherein the gene or gene product is CSTF1 (SEQ ID NOs: 117, 118), PCK1 (SEQ ID NOs: 123, 124), VAPB (SEQ ID NOs: 129, 130), GNAS (SEQ ID NOs: 135, 136), BCAS1 (SEQ ID NOs: 115, 116), TMEPA1 (SEQ ID NOs: 125, 126), or STX16 (SEQ ID NOs: 131, 132).
 9. The method of claim 8, wherein the gene or gene product is GNAS (SEQ ID NOs: 135, 136), TMEPA1 (SEQ ID NOs: 125, 126), STX16 (SEQ ID NOs: 131, 132), or VAPB (SEQ ID NOs: 129, 130).
 10. The method of claim 8, wherein the breast cancer is a luminal A breast cancer.
 11. The method of claim 1, wherein the detecting step comprises use a of methodology selected from the group consisting of quantitative PCR, FISH, array CGH, quantitative PCR, in situ hybridization for RNA, immunohistochemistry and reverse phase protein lysate arrays for protein.
 12. A method for selecting a patient for treatment with a drug that modulates the expression of a gene set forth in Table 3, said method comprising: providing tissue biopsy from the patient; determining from the provided tissue, the level of gene amplification or expression for a gene set forth in Table 3; identifying that one or more of the genes is amplified or expression increased; whereby, when the one or more genes are amplified or expression increased, this patent is a candidate for treatment with a drug that modulates the expression of the one or more gene of Table
 3. 13. The method of claim 12, wherein the gene is ACACA (SEQ ID NOs: 1, 2), ADAM9 (SEQ ID NOs: 3-8), ERBB2 (SEQ ID NOs: 9-14), FGFR1 (SEQ ID NOs: 15, 16), FNTA (SEQ ID NOs: 17, 18), IKBKB (SEQ ID NOs: 19, 20), NR1D1 (SEQ ID NOs: 21, 22), PNMT (SEQ ID NOs: 23, 24), or PROSC (SEQ ID NOs: 25, 26).
 14. The method of claim 13, wherein the gene is PROSC (SEQ ID NOs: 25, 26), ADAM9 (SEQ ID NOs: 3-8), FNTA (SEQ ID NOs: 17, 18), ACACA (SEQ ID NOs: 1, 2), PNMT (SEQ ID NOs: 23, 24), or NR1D1 (SEQ ID NOs: 21, 22).
 15. The method of claim 12, with the proviso that the gene is not ERBB2(SEQ ID NOs: 9-14), FGFR1 (SEQ ID NOs: 15, 16), or IKBKB (SEQ ID NOs: 19, 20).
 16. The method of claim 12, wherein the gene or gene product is ADAM9 (SEQ ID NOs: 3-8).
 17. The method of claim 12, wherein the gene or gene product is FGF3 (SEQ ID NOs: 65, 66), PPFIA1 (SEQ ID NOs: 69, 70), or NEU3 (SEQ ID NOs: 79, 80).
 18. The method of claim 12, wherein the gene or gene product is CSTF1 (SEQ ID NOs: 117, 118), PCK1 (SEQ ID NOs: 123, 124), VAPB (SEQ ID NOs: 129, 130), GNAS (SEQ ID NOs: 135, 136), BCAS1 (SEQ ID NOs: 115, 116), TMEPA1 (SEQ ID NOs: 125, 126), or STX16 (SEQ ID NOs: 131, 132).
 19. The method of claim 18, wherein the gene or gene product is GNAS (SEQ ID NOs: 135, 136), TMEPA1 (SEQ ID NOs: 125, 126), STX16 (SEQ ID NOs: 131, 132), or VAPB (SEQ ID NOs: 129, 130).
 20. The method of claim 12, wherein the determining step comprises use a of methodology selected from the group consisting of quantitative PCR, FISH, array CGH, quantitative PCR, in situ hybridization for RNA, immunohistochemistry and reverse phase protein lysate arrays for protein.
 21. A method for treatment of a patient with breast cancer, said method comprising: providing tissue biopsy from the patient; determining from the provided tissue, the level of gene amplification or expression for a gene set forth in Table 3; identifying that one or more of the genes is amplified or expression increased; whereby, when the one or more genes are amplified or expression increased, this patent is treated with a drug that modulates the expression of the one or more gene.
 22. The method of claim 21, wherein the gene is ACACA (SEQ ID NOs: 1), ADAM9 (SEQ ID NOs: 3, 5, 7), ERBB2 (SEQ ID NOs: 9, 11, 13), FGFR1 (SEQ ID NO: 15), FNTA (SEQ ID NO: 17), IKBKB (SEQ ID NO: 19), NR1D1 (SEQ ID NO: 21), PNMT (SEQ ID Ns: 23), or PROSC (SEQ ID Ns: 25).
 23. The method of claim 22, wherein the gene is PROSC (SEQ ID NO: 25), ADAM9 (SEQ ID NOs: 3, 5, 7), FNTA (SEQ ID NO: 17), ACACA (SEQ ID NO: 1), PNMT (SEQ ID NO: 23), or NR1D1 (SEQ ID NO: 21).
 24. The method of claim 21, with the proviso that the gene is not ERBB2(SEQ ID NOs: 9, 11, 13), FGFR1 (SEQ ID NO: 15), or IKBKB (SEQ ID NO: 19).
 25. The method of claim 21, wherein the gene is ADAM9 (SEQ ID NOs: 3, 5, 7).
 26. The method of claim 21, wherein the drug is an antisense sequence for a gene of Table 3, and the particular antisense sequence corresponds to the one or more amplified genes identified in the identifying step.
 27. The method of claim 26, wherein the gene is FGF3 (SEQ ID NO: 65), PPFIA1 (SEQ ID NO: 69), or NEU3 (SEQ ID NO: 79).
 28. The method of claim 26, wherein the gene is CSTF1 (SEQ ID NO: 117), PCK1 (SEQ ID NO: 123), VAPB (SEQ ID NO: 129), GNAS (SEQ ID NO: 135), BCAS1 (SEQ ID NO: 115), TMEPA1 (SEQ ID NO: 125), or STX16 (SEQ ID NO: 131).
 29. The method of claim 28, wherein the gene is GNAS (SEQ ID NO: 135), TMEPA1 (SEq ID NO: 125), STX16 (SEQ ID NO: 131), or VAPB (SEQ ID NO: 129).
 30. The method of claim 21, wherein the determining step comprises use a of methodology selected from the group consisting of quantitative PCR, FISH, array CGH, quantitative PCR, in situ hybridization for RNA, immunohistochemistry and reverse phase protein lysate arrays for protein.
 31. A method for identifying a moiety that modulates a protein, said method comprising: providing a protein selected from the group consisting of PROSC, ADAM9, FNTA, ACACA, PNMT, and NR1D1; screening the provided protein with a candidate moiety; determining whether the candidate moiety modulates the protein; and, selecting a moiety that modulates the protein.
 32. The method of claim 31 wherein the determining step comprises determining whether a function of the protein is modulated.
 33. The method of claim 31 wherein the determining step comprises determining whether expression of the protein is modulated.
 34. A method for altering the function of a PROSC, ADAM9, FNTA, ACACA, PNMT, or NR1D1 protein in a living cell, said method comprising: providing a moiety of claim 31; administering the moiety to a living cell that expresses PROSC, ADAM9, FNTA, ACACA, PNMT, or NR1D1 protein corresponding to the moiety; whereby, the function of PROSC, ADAM9, FNTA, ACACA, PNMT, or NR1D1 in the cell is altered.
 35. A method for prognosing the outcome of a patient with breast cancer, said method comprising: providing breast cancer tissue from the patient; determining from the provided tissue, the level of gene deletion for at least one gene from amplicon 8p11-12; identifying that the at least one gene is deleted; whereby, when the at least one gene is deleted, this is an indication that the patient has the predicted disease free survival or probability for distant recurrence set forth in Table3.
 36. The method of claim 35, wherein the at least one gene from amplicon 8p11-12 is selected from the chromosome 8 genes set forth in Table
 3. 37. The method of claim 35, wherein the determining step comprises use a of methodology selected from the group consisting of quantitative PCR, FISH, array CGH, quantitative PCR, in situ hybridization for RNA, immunohistochemistry and reverse phase protein lysate arrays for protein. 